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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Somatic Mutation Profiling and Associations With Prognosis and Trastuzumab Benefit in Early Breast Cancer</title><author><name sortKey="Loi, Sherene" sort="Loi, Sherene" uniqKey="Loi S" first="Sherene" last="Loi">Sherene Loi</name></author><author><name sortKey="Michiels, Stefan" sort="Michiels, Stefan" uniqKey="Michiels S" first="Stefan" last="Michiels">Stefan Michiels</name></author><author><name sortKey="Lambrechts, Diether" sort="Lambrechts, Diether" uniqKey="Lambrechts D" first="Diether" last="Lambrechts">Diether Lambrechts</name></author><author><name sortKey="Fumagalli, Debora" sort="Fumagalli, Debora" uniqKey="Fumagalli D" first="Debora" last="Fumagalli">Debora Fumagalli</name></author><author><name sortKey="Claes, Bart" sort="Claes, Bart" uniqKey="Claes B" first="Bart" last="Claes">Bart Claes</name></author><author><name sortKey="Kellokumpu Lehtinen, Pirkko Liisa" sort="Kellokumpu Lehtinen, Pirkko Liisa" uniqKey="Kellokumpu Lehtinen P" first="Pirkko-Liisa" last="Kellokumpu-Lehtinen">Pirkko-Liisa Kellokumpu-Lehtinen</name></author><author><name sortKey="Bono, Petri" sort="Bono, Petri" uniqKey="Bono P" first="Petri" last="Bono">Petri Bono</name></author><author><name sortKey="Kataja, Vesa" sort="Kataja, Vesa" uniqKey="Kataja V" first="Vesa" last="Kataja">Vesa Kataja</name></author><author><name sortKey="Piccart, Martine J" sort="Piccart, Martine J" uniqKey="Piccart M" first="Martine J." last="Piccart">Martine J. Piccart</name></author><author><name sortKey="Joensuu, Heikki" sort="Joensuu, Heikki" uniqKey="Joensuu H" first="Heikki" last="Joensuu">Heikki Joensuu</name></author><author><name sortKey="Sotiriou, Christos" sort="Sotiriou, Christos" uniqKey="Sotiriou C" first="Christos" last="Sotiriou">Christos Sotiriou</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">23739063</idno><idno type="pmc">3699437</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3699437</idno><idno type="RBID">PMC:3699437</idno><idno type="doi">10.1093/jnci/djt121</idno><date when="2013">2013</date><idno type="wicri:Area/Pmc/Corpus">000293</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000293</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Somatic Mutation Profiling and Associations With Prognosis and Trastuzumab Benefit in Early Breast Cancer</title><author><name sortKey="Loi, Sherene" sort="Loi, Sherene" uniqKey="Loi S" first="Sherene" last="Loi">Sherene Loi</name></author><author><name sortKey="Michiels, Stefan" sort="Michiels, Stefan" uniqKey="Michiels S" first="Stefan" last="Michiels">Stefan Michiels</name></author><author><name sortKey="Lambrechts, Diether" sort="Lambrechts, Diether" uniqKey="Lambrechts D" first="Diether" last="Lambrechts">Diether Lambrechts</name></author><author><name sortKey="Fumagalli, Debora" sort="Fumagalli, Debora" uniqKey="Fumagalli D" first="Debora" last="Fumagalli">Debora Fumagalli</name></author><author><name sortKey="Claes, Bart" sort="Claes, Bart" uniqKey="Claes B" first="Bart" last="Claes">Bart Claes</name></author><author><name sortKey="Kellokumpu Lehtinen, Pirkko Liisa" sort="Kellokumpu Lehtinen, Pirkko Liisa" uniqKey="Kellokumpu Lehtinen P" first="Pirkko-Liisa" last="Kellokumpu-Lehtinen">Pirkko-Liisa Kellokumpu-Lehtinen</name></author><author><name sortKey="Bono, Petri" sort="Bono, Petri" uniqKey="Bono P" first="Petri" last="Bono">Petri Bono</name></author><author><name sortKey="Kataja, Vesa" sort="Kataja, Vesa" uniqKey="Kataja V" first="Vesa" last="Kataja">Vesa Kataja</name></author><author><name sortKey="Piccart, Martine J" sort="Piccart, Martine J" uniqKey="Piccart M" first="Martine J." last="Piccart">Martine J. Piccart</name></author><author><name sortKey="Joensuu, Heikki" sort="Joensuu, Heikki" uniqKey="Joensuu H" first="Heikki" last="Joensuu">Heikki Joensuu</name></author><author><name sortKey="Sotiriou, Christos" sort="Sotiriou, Christos" uniqKey="Sotiriou C" first="Christos" last="Sotiriou">Christos Sotiriou</name></author></analytic><series><title level="j">JNCI Journal of the National Cancer Institute</title><idno type="ISSN">0027-8874</idno><idno type="eISSN">1460-2105</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Certain somatic alterations in breast cancer can define prognosis and response to therapy. This study investigated the frequencies, prognostic effects, and predictive effects of known cancer somatic mutations using a randomized, adjuvant, phase III clinical trial dataset.</p></sec><sec sec-type="methods"><title>Methods</title><p>The FinHER trial was a phase III, randomized adjuvant breast cancer trial involving 1010 women. Patients with human epidermal growth factor receptor 2 (HER2)–positive breast cancer were further randomized to 9 weeks of trastuzumab or no trastuzumab. Seven hundred five of 1010 tumors had sufficient DNA for genotyping of 70 somatic hotspot mutations in 20 genes using mass spectrometry. Distant disease-free survival (DDFS), overall survival (OS), and interactions with trastuzumab were explored with Kaplan-Meier and Cox regression analyses. All statistical tests were two-sided.</p></sec><sec><title>Results</title><p>Median follow-up was 62 months. Of 705 tumors, 687 were successfully genotyped. <italic>PIK3CA</italic> mutations (exons 1, 2, 4, 9, 13, 18, and 20) were present in 25.3% (174 of 687) and <italic>TP53</italic> mutations in 10.2% (70 of 687). Few other mutations were found: three <italic>ERBB2</italic> and single cases of <italic>KRAS</italic>, <italic>ALK, STK11/LKB1</italic>, and <italic>AKT2</italic>. <italic>PIK3CA</italic> mutations were associated with estrogen receptor positivity (<italic>P</italic> < .001) and the luminal-A phenotype (<italic>P</italic> = .04) but were not statistically significantly associated with prognosis (DDFS: hazard ratio [HR] = 0.88, 95% confidence [CI] = 0.58 to 1.34, <italic>P</italic> = .56; OS: HR = 0.603, 95% CI = .32 to 1.13, <italic>P</italic> = .11), although a statistically significant nonproportional prognostic effect was observed for DDFS (<italic>P</italic> = .002). <italic>PIK3CA</italic> mutations were not statistically significantly associated with trastuzumab benefit (<italic>P</italic><sub>interaction</sub>: DDFS <italic>P</italic> = .14; OS <italic>P</italic> = .24).</p></sec><sec><title>Conclusions</title><p>In this dataset, targeted genotyping revealed only two alterations at a frequency greater than 10%, with other mutations observed infrequently. <italic>PIK3CA</italic> mutations were associated with a better outcome, however this effect disappeared after 3 years. There were no statistically significant associations with trastuzumab benefit.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Perou, Cm" uniqKey="Perou C">CM Perou</name></author><author><name sortKey="Sorlie, T" uniqKey="Sorlie T">T Sorlie</name></author><author><name sortKey="Eisen, Mb" uniqKey="Eisen M">MB Eisen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Slamon, Dj" uniqKey="Slamon D">DJ Slamon</name></author><author><name sortKey="Clark, Gm" uniqKey="Clark G">GM Clark</name></author><author><name sortKey="Wong, Sg" uniqKey="Wong S">SG Wong</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Engelman, Ja" uniqKey="Engelman J">JA Engelman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cizkova, M" uniqKey="Cizkova M">M Cizkova</name></author><author><name sortKey="Susini, A" uniqKey="Susini A">A 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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Natl Cancer Inst</journal-id><journal-id journal-id-type="iso-abbrev">J. Natl. Cancer Inst</journal-id><journal-id journal-id-type="hwp">jnci</journal-id><journal-id journal-id-type="publisher-id">jnci.j</journal-id><journal-title-group><journal-title>JNCI Journal of the National Cancer Institute</journal-title></journal-title-group><issn pub-type="ppub">0027-8874</issn><issn pub-type="epub">1460-2105</issn><publisher><publisher-name>Oxford University Press</publisher-name><publisher-loc>US</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">23739063</article-id><article-id pub-id-type="pmc">3699437</article-id><article-id pub-id-type="doi">10.1093/jnci/djt121</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Somatic Mutation Profiling and Associations With Prognosis and Trastuzumab Benefit in Early Breast Cancer</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Loi</surname><given-names>Sherene</given-names></name><xref ref-type="corresp" rid="c2">*</xref></contrib><contrib contrib-type="author"><name><surname>Michiels</surname><given-names>Stefan</given-names></name></contrib><contrib contrib-type="author"><name><surname>Lambrechts</surname><given-names>Diether</given-names></name></contrib><contrib contrib-type="author"><name><surname>Fumagalli</surname><given-names>Debora</given-names></name></contrib><contrib contrib-type="author"><name><surname>Claes</surname><given-names>Bart</given-names></name></contrib><contrib contrib-type="author"><name><surname>Kellokumpu-Lehtinen</surname><given-names>Pirkko-Liisa</given-names></name></contrib><contrib contrib-type="author"><name><surname>Bono</surname><given-names>Petri</given-names></name></contrib><contrib contrib-type="author"><name><surname>Kataja</surname><given-names>Vesa</given-names></name></contrib><contrib contrib-type="author"><name><surname>Piccart</surname><given-names>Martine J.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Joensuu</surname><given-names>Heikki</given-names></name></contrib><contrib contrib-type="author"><name><surname>Sotiriou</surname><given-names>Christos</given-names></name></contrib><aff id="AF0001"><bold>Affiliations of authors:</bold><institution>Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles</institution>,<addr-line>Brussels</addr-line>,<country>Belgium</country> (SL, SM, DF, CS);<institution>Department of Biostatistics and Epidemiology, Institut Gustave Roussy</institution>,<addr-line>Villejuif</addr-line>,<country>France</country> (SM);<institution>Vesalius Research Centre, KU Leuven and VIB</institution>,<addr-line>Leuven</addr-line>,<country>Belgium</country> (DL, BC);<institution>Laboratory for Translational Genetics, Department of Oncology, University of Leuven</institution>,<addr-line>Leuven</addr-line>,<country>Belgium</country> (DL, BC);<institution>Department of Oncology, Tampere University Hospital</institution>,<addr-line>Tampere</addr-line>,<country>Finland</country> (P-LK-L);<institution>Department of Oncology, Helsinki University Central Hospital</institution>,<addr-line>Helsinki</addr-line>,<country>Finland</country> (PB, HJ);<institution>Cancer Center, Kuopio University Hospital</institution>,<addr-line>Kuopio</addr-line>,<country>Finland</country> (VK);<institution>Department of Medicine, Institut Jules Bordet</institution>,<addr-line>Brussels</addr-line>,<country>Belgium</country> (MJP).</aff></contrib-group><author-notes><corresp id="c2">*Present affiliation: Translational Breast Cancer Genomics Lab, Division of Research, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia.
</corresp><corresp id="c1"><bold>Correspondence to:</bold> Sherene Loi, MD, PhD, Peter MacCallum Cancer Centre, Department of Medical Oncology, Locked Bag 1, A’ Beckett St, Melbourne, VIC 8006, Australia (e-mail: <email>sherene.loi@petermac.org</email>).</corresp></author-notes><pub-date pub-type="ppub"><day>3</day><month>7</month><year>2013</year></pub-date><pub-date pub-type="epub"><day>5</day><month>6</month><year>2013</year></pub-date><pub-date pub-type="pmc-release"><day>5</day><month>6</month><year>2013</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<volume>105</volume><issue>13</issue><fpage>960</fpage><lpage>967</lpage><history><date date-type="received"><day>7</day><month>11</month><year>2012</year></date><date date-type="rev-recd"><day>14</day><month>3</month><year>2013</year></date><date date-type="accepted"><day>18</day><month>3</month><year>2013</year></date></history><permissions><copyright-statement>© The Author 2013. Published by Oxford University Press.</copyright-statement><copyright-year>2013</copyright-year><license license-type="creative-commons"><license-p><pmc-comment>CREATIVE COMMONS</pmc-comment>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/">http://creativecommons.org/licenses/by-nc/3.0/</ext-link>), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com</license-p></license></permissions><abstract><sec><title>Background</title><p>Certain somatic alterations in breast cancer can define prognosis and response to therapy. This study investigated the frequencies, prognostic effects, and predictive effects of known cancer somatic mutations using a randomized, adjuvant, phase III clinical trial dataset.</p></sec><sec sec-type="methods"><title>Methods</title><p>The FinHER trial was a phase III, randomized adjuvant breast cancer trial involving 1010 women. Patients with human epidermal growth factor receptor 2 (HER2)–positive breast cancer were further randomized to 9 weeks of trastuzumab or no trastuzumab. Seven hundred five of 1010 tumors had sufficient DNA for genotyping of 70 somatic hotspot mutations in 20 genes using mass spectrometry. Distant disease-free survival (DDFS), overall survival (OS), and interactions with trastuzumab were explored with Kaplan-Meier and Cox regression analyses. All statistical tests were two-sided.</p></sec><sec><title>Results</title><p>Median follow-up was 62 months. Of 705 tumors, 687 were successfully genotyped. <italic>PIK3CA</italic> mutations (exons 1, 2, 4, 9, 13, 18, and 20) were present in 25.3% (174 of 687) and <italic>TP53</italic> mutations in 10.2% (70 of 687). Few other mutations were found: three <italic>ERBB2</italic> and single cases of <italic>KRAS</italic>, <italic>ALK, STK11/LKB1</italic>, and <italic>AKT2</italic>. <italic>PIK3CA</italic> mutations were associated with estrogen receptor positivity (<italic>P</italic> < .001) and the luminal-A phenotype (<italic>P</italic> = .04) but were not statistically significantly associated with prognosis (DDFS: hazard ratio [HR] = 0.88, 95% confidence [CI] = 0.58 to 1.34, <italic>P</italic> = .56; OS: HR = 0.603, 95% CI = .32 to 1.13, <italic>P</italic> = .11), although a statistically significant nonproportional prognostic effect was observed for DDFS (<italic>P</italic> = .002). <italic>PIK3CA</italic> mutations were not statistically significantly associated with trastuzumab benefit (<italic>P</italic><sub>interaction</sub>: DDFS <italic>P</italic> = .14; OS <italic>P</italic> = .24).</p></sec><sec><title>Conclusions</title><p>In this dataset, targeted genotyping revealed only two alterations at a frequency greater than 10%, with other mutations observed infrequently. <italic>PIK3CA</italic> mutations were associated with a better outcome, however this effect disappeared after 3 years. There were no statistically significant associations with trastuzumab benefit.</p></sec></abstract><counts><page-count count="8"></page-count></counts></article-meta></front><body><p>Gene expression profiling divides breast cancer into distinct molecular portraits according to the presence of the estrogen receptor (ER) and amplification/overexpression of the <italic>ERBB2/</italic>HER2/<italic>neu</italic> oncogene (<xref rid="CIT0001" ref-type="bibr">1</xref>). Notably, HER2 amplification/overexpression (HER2-positive) predicts response to anti-HER2 therapy, suggesting that somatic alterations in breast cancer are associated with prognosis and potentially amenable to targeted therapy (<xref rid="CIT0002" ref-type="bibr">2</xref>). This has inspired efforts to better understand the spectrum of somatic “driver” mutations and, in particular, targetable mutated kinases.</p><p content-type="indent">An abundance of data suggests that genetic aberrations and activation of the phosphatidylinositol 3-kinase (PI3K) pathway are important in determining breast cancer prognosis and the efficacy of standard chemo- and endocrine therapies (<xref rid="CIT0003" ref-type="bibr">3</xref>). Furthermore, mutations in the <italic>PIK3CA</italic> gene, which encodes the p110α catalytic subunit of the class IA PI3K, are frequent in breast cancer (<xref rid="CIT0004" ref-type="bibr">4–7</xref>). These mutations have been shown to be oncogenic in mammary epithelial cells by driving constitutive, growth factor–independent PI3K pathway activation (<xref rid="CIT0008" ref-type="bibr">8</xref>,<xref rid="CIT0009" ref-type="bibr">9</xref>).</p><p content-type="indent">Despite being the focus of intense research interest, a clear association between <italic>PIK3CA</italic> mutations and a poorer prognosis has not been shown. To the contrary, <italic>PIK3CA</italic> mutations have been associated with statistically significantly better survival when compared with <italic>PIK3CA</italic> wild-type breast cancers in larger series obtained from single institutions (<xref rid="CIT0004" ref-type="bibr">4</xref>,<xref rid="CIT0007" ref-type="bibr">7–10</xref>). An association with resistance to endocrine therapy has also not been demonstrated (<xref rid="CIT0006" ref-type="bibr">6</xref>,<xref rid="CIT0011" ref-type="bibr">11</xref>,<xref rid="CIT0012" ref-type="bibr">12</xref>). <italic>PIK3CA</italic> mutations have also been shown to be associated with trastuzumab resistance in preclinical models overexpressing HER2 (<xref rid="CIT0013" ref-type="bibr">13–15</xref>). Clinical validation of this association could have important clinical utility given the emergence of a broadening array of anti-HER2 agents and the concept of dual anti-HER2 therapy (<xref rid="CIT0016" ref-type="bibr">16–18</xref>). Hence, given their frequency, oncogenic capabilities, and the potential to induce resistance to commonly prescribed breast cancer treatments, the clinical relevance of <italic>PIK3CA</italic> mutations deserves further clarification.</p><p content-type="indent">High levels of evidence on the clinical utility of prognostic and predictive biomarkers can be achieved from the use of archived tumor specimens from appropriate randomized clinical trial datasets (<xref rid="CIT0019" ref-type="bibr">19</xref>). Therefore, the main purpose of this study was to clarify in a well-characterized, randomized clinical trial dataset the predictive relevance of <italic>PIK3CA</italic> mutations to trastuzumab efficacy and its prognostic abilities in both HER2-positive and HER2-negative disease. Given that <italic>PIK3CA</italic> genotyping can be performed with other somatic hotspot mutations, we also set out to determine prevalence and prognostic associations of other known cancer driver mutations. Our objective was to identify other potentially targetable genetic alterations that contribute to resistance to standard therapy in breast cancer.</p><sec sec-type="methods" id="s1"><title>Methods</title><p>The Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) criteria were followed in this study (<xref rid="CIT0020" ref-type="bibr">20</xref>).</p><sec id="s2"><title>Patients in the FinHER Study</title><p>This study is based on formalin-fixed, paraffin-embedded (FFPE) primary breast tumor tissue samples of Finnish women who were aged <66 years and diagnosed with either node-positive or node-negative breast cancer and who participated in the FinHER trial (N = 1010) from 2000 to 2003, a multicenter adjuvant trial sponsored by the Finnish Breast Cancer Group (<xref rid="CIT0021" ref-type="bibr">21</xref>,<xref rid="CIT0022" ref-type="bibr">22</xref>). All women were randomly assigned to receive three cycles of docetaxel or vinorelbine, followed by (in both groups) three cycles of fluorouracil, epirubicin, and cyclophosphamide. The 232 women whose tumors had an amplified HER2/<italic>neu</italic> gene were further randomly assigned to receive or to not receive nine weekly trastuzumab infusions. The primary end point of the FinHER study, distant disease-free survival (DDFS), has been previously reported to be superior for the docetaxel- and trastuzumab-containing arms. The final median follow-up was 62 months (<xref rid="CIT0021" ref-type="bibr">21</xref>).</p><p content-type="indent">The determination of steroid hormone receptor status and HER2 expression by immunohistochemistry (IHC) was required locally and was performed according to the guidelines of each institution during the time of the study. Samples were considered hormone receptor positive if their level of ER and/or progesterone receptor (PR) was ≥10%. All patients with ER- or PR-positive tumors received five years of endocrine therapy, which was tamoxifen when the study commenced, but aromatase inhibitors were permitted midway through the study. Ki67 IHC was assessed in locally by IHC using the Mib-1 monoclonal antibody (Dako, Glostrup, Denmark). When HER2 expression was scored as 2+ or 3+ (on a scale of 0, 1+, 2+, or 3+), the number of copies of the HER2/<italic>neu</italic> gene was centrally determined by means of chromogenic in situ hybridization in one of two reference laboratories.</p></sec><sec id="s3"><title>Mutation Testing</title><p>Study participants provided written informed consent to allow further research analyses to be carried out on their tumor tissue. The ethical committee of the Helsinki University Central Hospital also approved the current study. Of the 1010 samples, 935 (92.5%) could be retrieved. All samples were reevaluated by one reference pathologist to ensure tumor was present in the tissue sample. Of these samples, 705 (75.4%) were able to have adequate DNA extracted. DNA was extracted from FFPE tumor tissue using a salt precipitation method (<xref rid="CIT0023" ref-type="bibr">23</xref>).</p><p content-type="indent">Because few data were available about the mutational landscape of breast cancer at the time of genotyping, we queried the COSMIC (Catalogue of Somatic Mutations in Cancer) database to identify a broad range of genes for hotspot somatic mutation profiling (<xref rid="CIT0024" ref-type="bibr">24</xref>). We ultimately covered 94% of reported <italic>PIK3CA</italic> mutations (exons 1, 2, 4, 9, 13, 18, 20) reported in COSMIC to be occurring in breast cancer in this study, 12% of <italic>TP53</italic> mutations (all cancer types), selected <italic>ERBB2</italic>, <italic>PTEN, AKT1/2</italic> mutations, and known <italic>EGFR, BRAF, KRAS, MAP3K1</italic>, and <italic>CDK4</italic> mutations, as well as a scattering of other “druggable” mutations. In total, 70 mutations in 20 genes were evaluated (<ext-link ext-link-type="uri" xlink:href="http://jnci.oxfordjournals.org/lookup/suppl/doi:10.1093/jnci/djt121/-/DC1">Supplementary Table 1</ext-link>, available online).</p><p content-type="indent">The samples were genotyped centrally using the Sequenom MassARRAY Assay Design 3.1 with the default parameters. Multiplex polymerase chain reaction was done in 5-µL volume containing 5–10ng of DNA. Samples were considered to be of sufficient quality when genotyping could be performed for >75% of the mutations. A total of 687 samples (68.1% of the original FinHER cohort) were successfully genotyped (2.5% [18 of 705] samples were discarded for this reason). Sixteen samples were genotyped in duplicate and were found to have 100% concordance. Details about the assay and independent validation have been previously published: the Sequenom can detect low-frequency mutant alleles to maximize mutation detection in impure samples (≥5% for the PIK3CA hotspot mutations) with sensitivity and specificity of 90% and 99%, respectively, in FFPE-derived DNA, and 100% concordance with other technologies (<xref rid="CIT0025" ref-type="bibr">25</xref>,<xref rid="CIT0026" ref-type="bibr">26</xref>). In this study, we further confirmed one sample of each positive <italic>PIK3CA</italic> mutation, as well as a wild-type sample, using Sanger sequencing (except for the rarer G241A, G3019C, and C473T); another 9 samples (both positive and wild type) were confirmed with the Qiagen Rotor-gene Kit. All of these were found to be 100% concordant with the Sequenom results<italic>. ERBB2</italic> mutations were also confirmed using Sanger sequencing (primers TCCTGGAAGGCAGGTAGGATCCAG and AGTCTAGGTTTGCGGGAGTCATATCTC).</p></sec><sec id="s4"><title>Statistical Analysis</title><p>In this study, a sample was considered to be wild type for a given gene if no mutation was found. Associations between mutations and clinicopathologic characteristics were investigated with χ<sup>2</sup> tests for categorical variables. For the survival analyses, the primary end point was DDFS, which was defined as the time period from the date of random treatment assignment to the date of first cancer recurrence outside the ipsilateral locoregional region or to death, whenever death occurred before distant recurrence (<xref rid="CIT0021" ref-type="bibr">21</xref>). Relapse-free survival (RFS) was defined as the time from the date of random assignment to the date of the local, distant, or contralateral invasive cancer recurrence or death. Overall survival (OS) was defined as the time period from the date of random assignment to the date of death, whenever death occurred before distant recurrence.</p><p content-type="indent">Cox proportional hazards regression models were used to test the prognostic value of <italic>PIK3CA</italic> mutation status (hazard ratios [HRs] and 95% confidence intervals [CIs]) and its possible interaction with trastuzumab treatment (after adding a trastuzumab main effect and a product interaction term) using the Wald test. The Cox models used a separate baseline hazard for chemotherapy type (docetaxel or vinorelbine). Departures from the proportional hazards assumption were assessed based on the Schoenfeld residuals (<xref rid="CIT0027" ref-type="bibr">27</xref>).</p><p content-type="indent">All <italic>P</italic> values were two-sided and a <italic>P</italic> value of less than .05 was considered statistically significant. The Kaplan-Meier survival curves were calculated (with group differences assessed using the log-rank test). Interaction effects were also displayed using forest plots. No adjustment was planned for multiple testing of the prespecified hypotheses. For this study, breast cancer subtypes were classified using IHC as previously published (<xref rid="CIT0028" ref-type="bibr">28</xref>): luminal (ER-positive and/or progesteron receptor [PgR]–positive, HER2-negative), HER2-positive/overexpressing by (chromogenic in situ hybridization), and triple negative: ER-negative/PgR-negative/HER2-negative. Luminal A and B phenotypes were defined using IHC staining of Ki67-positive cells using a cutoff of less than 14% and greater than 14%, respectively (<xref rid="CIT0028" ref-type="bibr">28</xref>).</p></sec></sec><sec id="s5"><title>Results</title><sec id="s6"><title>Patient Characteristics</title><p>The patient characteristics of the genotyped series (n = 687) are compared with the original series and those who were not genotyped in <ext-link ext-link-type="uri" xlink:href="http://jnci.oxfordjournals.org/lookup/suppl/doi:10.1093/jnci/djt121/-/DC1">Supplementary Table 2</ext-link> (available online). There were more tumors that were ER-negative, of larger size and higher grade, and from younger patients genotyped compared with those not genotyped. There were no statistically significant differences in survival between groups (DDFS log-rank <italic>P</italic> = .19, RFS <italic>P</italic> = .34, OS <italic>P</italic> = .64).</p></sec><sec id="s7"><title>Frequency and Associations Between Mutations</title><p>Despite genotyping this cohort for 70 known cancer somatic “driver” mutations in 20 genes, only <italic>PIK3CA</italic> and <italic>TP53</italic> somatic mutations occurred at frequencies >10%.</p><p content-type="indent"><italic>PIK3CA</italic> mutations were successfully genotyped in 100% of samples that passed the quality control criteria. 176 <italic>PIK3CA</italic> mutations were found (<ext-link ext-link-type="uri" xlink:href="http://jnci.oxfordjournals.org/lookup/suppl/doi:10.1093/jnci/djt121/-/DC1">Supplementary Table 3</ext-link>, available online). The vast majority of these were located on the hotspots on the helical and kinase domains (exons 9 and 20, respectively—161 of 176 [91.5%]), with two samples having a double <italic>PIK3CA</italic> mutation present (A3140G + C473T; T1035A + G1633A). The overall frequency of tumor samples with a PIK3CA mutation was 25.3% (174 of 687). <italic>TP53</italic> mutations, with coverage of approximately 12% of known mutations, were present in 10.2% (70 of 687) of samples. Three <italic>ERBB2</italic> kinase domain mutations (two *T2264C, C2313T) were present in 0.5% of samples genotyped (3 of 659 [28 of 687 samples could not be assigned]). Mutations that occurred only once were <italic>KRAS</italic> (G35A), <italic>AKT2</italic> (G49A), <italic>ALK</italic> (G3824A), and <italic>STK11/LKB1</italic> (C1062G) (<xref ref-type="fig" rid="F1">Figure 1</xref>).</p><fig fig-type="figure" id="F1" position="float"><label>Figure 1.</label><caption><p>Frequency and associations between mutations. Absolute numbers are shown of <italic>PIK3CA</italic> mutant, <italic>PIK3CA</italic> wild type, <italic>ERBB2</italic> mutant, and <italic>TP53</italic> mutant, as well as those tumors with coexisting mutations. <italic>PIK3CA</italic> exon 9 and 20 mutations (and other locations) are also shown.</p></caption><graphic xlink:href="jnci.j_djt121_f0001"></graphic></fig></sec><sec id="s8"><title>Association With Clinicopathological Features and Breast Cancer Subtypes</title><p><italic>PIK3CA</italic> mutations were statistically significantly associated with smaller tumor size (T1, <italic>P</italic> = .03), histological grade 1 (<italic>P</italic> < .001), positive expression of the ER (<italic>P</italic> < .001), and the luminal-A phenotype (<italic>P</italic> = .04; <xref ref-type="table" rid="T1">Table 1</xref>). As expected, <italic>TP53</italic> mutations were associated with ER negativity (<italic>P</italic> = .005), histological grade 3 (<italic>P</italic> = .007), larger tumor size (<italic>P</italic> = .009), and four or more positive lymph nodes (<italic>P</italic> = .003). All three <italic>ERBB2</italic> mutant samples were ER-positive and HER2-negative (luminal). In the three main breast cancer subtypes defined using IHC, as expected, <italic>PIK3CA</italic> mutations were highly frequent in luminal and HER2-positive subtypes (<italic>P</italic> < .001) and TP53 mutations in the triple-negative group (<italic>P</italic> = .003; <xref ref-type="table" rid="T2">Table 2</xref>).</p><table-wrap id="T1" position="float"><label>Table 1.</label><caption><p>Patient and tumor characteristics by PIK3CA genotype*</p></caption><table frame="vsides" rules="groups"><thead><tr><th rowspan="2" align="left" valign="bottom" colspan="1">Characteristic</th><th align="center" rowspan="2" valign="bottom" colspan="1">Whole cohort (N = 687)</th><th align="center" colspan="3" valign="bottom" rowspan="1">PIK3CA genotype</th><th align="center" colspan="3" valign="bottom" rowspan="1">TP53 genotype</th></tr><tr><th align="center" valign="bottom" rowspan="1" colspan="1">WT (n = 511)</th><th align="center" valign="bottom" rowspan="1" colspan="1">Any mt PIK3CA (n = 176)</th><th align="center" valign="bottom" rowspan="1" colspan="1"><italic>P</italic></th><th align="center" valign="bottom" rowspan="1" colspan="1">WT (n = 617)</th><th align="center" valign="bottom" rowspan="1" colspan="1">Any mt TP53 (n = 70)</th><th align="center" valign="bottom" rowspan="1" colspan="1"><italic>P</italic></th></tr></thead><tbody><tr><td colspan="8" rowspan="1">Age category</td></tr><tr><td rowspan="1" colspan="1"> ≤50 y</td><td align="center" rowspan="1" colspan="1">364 (53%)</td><td align="center" rowspan="1" colspan="1">274 (53.6%)</td><td align="center" rowspan="1" colspan="1">90 (51.1%)</td><td align="center" rowspan="1" colspan="1">.57</td><td align="center" rowspan="1" colspan="1">330 (53.5%)</td><td align="center" rowspan="1" colspan="1">34 (48.6%)</td><td align="center" rowspan="1" colspan="1">.44</td></tr><tr><td rowspan="1" colspan="1"> >50 y</td><td align="center" rowspan="1" colspan="1">323 (47%)</td><td align="center" rowspan="1" colspan="1">237 (46.4%)</td><td align="center" rowspan="1" colspan="1">86 (48.9%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">287 (46.5%)</td><td align="center" rowspan="1" colspan="1">36 (51.4%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td colspan="8" rowspan="1">Tumor stage</td></tr><tr><td rowspan="1" colspan="1"> T1</td><td align="center" rowspan="1" colspan="1">275 (40%)</td><td align="center" rowspan="1" colspan="1">192 (37.8%)</td><td align="center" rowspan="1" colspan="1">83 (47.2%)</td><td align="center" rowspan="1" colspan="1">.003</td><td align="center" rowspan="1" colspan="1">258 (42%)</td><td align="center" rowspan="1" colspan="1">17 (24.3%)</td><td align="center" rowspan="1" colspan="1">.009</td></tr><tr><td rowspan="1" colspan="1"> T2</td><td align="center" rowspan="1" colspan="1">364 (53%)</td><td align="center" rowspan="1" colspan="1">274 (53.9%)</td><td align="center" rowspan="1" colspan="1">90 (51.1%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">319 (52%)</td><td align="center" rowspan="1" colspan="1">45 (64.3%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> T3</td><td align="center" rowspan="1" colspan="1">45 (6.6%)</td><td align="center" rowspan="1" colspan="1">42 (8.3%)</td><td align="center" rowspan="1" colspan="1">3 (1.7%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">37 (6%)</td><td align="center" rowspan="1" colspan="1">8 (11.4%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> Missing</td><td align="center" rowspan="1" colspan="1">3 (0.4%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td colspan="8" rowspan="1">Nodal status</td></tr><tr><td rowspan="1" colspan="1"> Negative</td><td align="center" rowspan="1" colspan="1">81 (11.8%)</td><td align="center" rowspan="1" colspan="1">64 (12.5%)</td><td align="center" rowspan="1" colspan="1">17 (9.7%)</td><td align="center" rowspan="1" colspan="1">.33</td><td align="center" rowspan="1" colspan="1">64 (10.4%)</td><td align="center" rowspan="1" colspan="1">17 (24.3%)</td><td align="center" rowspan="1" colspan="1">.003</td></tr><tr><td rowspan="1" colspan="1"> 1–3</td><td align="center" rowspan="1" colspan="1">410 (59.7%)</td><td align="center" rowspan="1" colspan="1">297 (58.1%)</td><td align="center" rowspan="1" colspan="1">113 (64.2%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">373 (60.5%)</td><td align="center" rowspan="1" colspan="1">37 (52.9%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> >3</td><td align="center" rowspan="1" colspan="1">196 (28.5%)</td><td align="center" rowspan="1" colspan="1">150 (29.4%)</td><td align="center" rowspan="1" colspan="1">46 (26.1%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">180 (29.2%)</td><td align="center" rowspan="1" colspan="1">16 (22.9%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td colspan="8" rowspan="1">Histological grade </td></tr><tr><td rowspan="1" colspan="1"> I</td><td align="center" rowspan="1" colspan="1">80 (11.6%)</td><td align="center" rowspan="1" colspan="1">46 (9.3%)</td><td align="center" rowspan="1" colspan="1">34 (20.2%)</td><td align="center" rowspan="1" colspan="1"><.001</td><td align="center" rowspan="1" colspan="1">73 (12.2%)</td><td align="center" rowspan="1" colspan="1">7 (8.8%)</td><td align="center" rowspan="1" colspan="1">.007</td></tr><tr><td rowspan="1" colspan="1"> II</td><td align="center" rowspan="1" colspan="1">270 (39.3%)</td><td align="center" rowspan="1" colspan="1">187 (37.8%)</td><td align="center" rowspan="1" colspan="1">83 (49.4%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">254 (42.5%)</td><td align="center" rowspan="1" colspan="1">16 (24.2%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> III</td><td align="center" rowspan="1" colspan="1">313 (96.5%)</td><td align="center" rowspan="1" colspan="1">262 (52.9%)</td><td align="center" rowspan="1" colspan="1">51 (30.4%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">270 (45.2%)</td><td align="center" rowspan="1" colspan="1">43 (54.2%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> Missing</td><td align="center" rowspan="1" colspan="1">23 (3.5%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td colspan="8" rowspan="1">ER IHC</td></tr><tr><td rowspan="1" colspan="1"> Positive</td><td align="center" rowspan="1" colspan="1">475 (69.1%)</td><td align="center" rowspan="1" colspan="1">335 (69.7%)</td><td align="center" rowspan="1" colspan="1">140 (79.5%)</td><td align="center" rowspan="1" colspan="1"><.001</td><td align="center" rowspan="1" colspan="1">437 (70.8%)</td><td align="center" rowspan="1" colspan="1">32 (45.7%)</td><td align="center" rowspan="1" colspan="1">.005</td></tr><tr><td rowspan="1" colspan="1"> Negative</td><td align="center" rowspan="1" colspan="1">212 (30.9%)</td><td align="center" rowspan="1" colspan="1">176 (26.3%)</td><td align="center" rowspan="1" colspan="1"> 36 (20.5%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">180 (29.2%)</td><td align="center" rowspan="1" colspan="1">38 (45.7%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1">HER2 amplification</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> Positive </td><td align="center" rowspan="1" colspan="1">157 (22.9%)</td><td align="center" rowspan="1" colspan="1">123 (24.1%)</td><td align="center" rowspan="1" colspan="1">34 (19.3%)</td><td align="center" rowspan="1" colspan="1">.20</td><td align="center" rowspan="1" colspan="1">138 (22.4%)</td><td align="center" rowspan="1" colspan="1">19 (27.1%)</td><td align="center" rowspan="1" colspan="1">.37</td></tr><tr><td rowspan="1" colspan="1"> Negative</td><td align="center" rowspan="1" colspan="1">530 (77.1%)</td><td align="center" rowspan="1" colspan="1">388 (68.8%)</td><td align="center" rowspan="1" colspan="1">142 (28.8%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">479 (77.6%)</td><td align="center" rowspan="1" colspan="1">51 (72.9%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1">Histology</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> Ductal</td><td align="center" rowspan="1" colspan="1">558 (81.2%)</td><td align="center" rowspan="1" colspan="1">422 (83.6%)</td><td align="center" rowspan="1" colspan="1">136 (78.6%)</td><td align="center" rowspan="1" colspan="1">.14</td><td align="center" rowspan="1" colspan="1">501 (82.3%)</td><td align="center" rowspan="1" colspan="1">57 (82.6%)</td><td align="center" rowspan="1" colspan="1">.94</td></tr><tr><td rowspan="1" colspan="1"> Lobular</td><td align="center" rowspan="1" colspan="1">120 (17.5%)</td><td align="center" rowspan="1" colspan="1">83 (16.4%)</td><td align="center" rowspan="1" colspan="1">37 (21.4%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">108 (17.7%)</td><td align="center" rowspan="1" colspan="1">12 (17.4%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> Other</td><td align="center" rowspan="1" colspan="1">9 (1.3%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td colspan="8" rowspan="1">Breast cancer subtype (defined by IHC)</td></tr><tr><td rowspan="1" colspan="1">Luminal (ER-positive/HER2-negative)</td><td align="center" rowspan="1" colspan="1">410 (59.7%)</td><td align="center" rowspan="1" colspan="1">284 (55.6%)</td><td align="center" rowspan="1" colspan="1">126 (71.6%)</td><td align="center" rowspan="1" colspan="1"><.001</td><td align="center" rowspan="1" colspan="1">380 (61.6%)</td><td align="center" rowspan="1" colspan="1">30 (42.9%)</td><td align="center" rowspan="1" colspan="1">.003</td></tr><tr><td rowspan="1" colspan="1"> HER2-amplified</td><td align="center" rowspan="1" colspan="1">157 (22.9%)</td><td align="center" rowspan="1" colspan="1">123 (24.1%)</td><td align="center" rowspan="1" colspan="1">4 (19.3%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">138 (22.4%)</td><td align="center" rowspan="1" colspan="1">19 (27.1%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1">Triple negative (ER-negative/PgR-negative/HER2-negative)</td><td align="center" rowspan="1" colspan="1">120 (17.5%)</td><td align="center" rowspan="1" colspan="1">104 (20.4%)</td><td align="center" rowspan="1" colspan="1">16 (9.1%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">99 (16%)</td><td align="center" rowspan="1" colspan="1">21 (30%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td colspan="8" rowspan="1">Luminal A/B </td></tr><tr><td rowspan="1" colspan="1"> Ki67 IHC <14%</td><td align="center" rowspan="1" colspan="1">127 (30%)</td><td align="center" rowspan="1" colspan="1">80 (31.7%)</td><td align="center" rowspan="1" colspan="1">47 (42.7%)</td><td align="center" rowspan="1" colspan="1">.04</td><td align="center" rowspan="1" colspan="1">121 (36.2%)</td><td align="center" rowspan="1" colspan="1">6 (21.4%)</td><td align="center" rowspan="1" colspan="1">.12</td></tr><tr><td rowspan="1" colspan="1"> Ki67 IHC≥14%</td><td align="center" rowspan="1" colspan="1">235 (57.3%)</td><td align="center" rowspan="1" colspan="1">172 (68.3%)</td><td align="center" rowspan="1" colspan="1">63 (57.3%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">213 (63.8%)</td><td align="center" rowspan="1" colspan="1">22 (78.6%)</td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> NA</td><td align="center" rowspan="1" colspan="1">48 (11.7%)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr></tbody></table><table-wrap-foot><fn fn-type="other"><p>* <italic>P</italic> values were calculated using a two-sided χ<sup>2</sup> test. ER = estrogen receptor; IHC = immunohistochemistry; mt = mutation; NA = not applicable; WT = wild type.</p></fn></table-wrap-foot></table-wrap><table-wrap id="T2" position="float"><label>Table 2.</label><caption><p>Frequency of mutations by breast cancer subtype*</p></caption><table frame="vsides" rules="groups"><thead><tr><th align="left" valign="bottom" rowspan="1" colspan="1">Subtype</th><th align="center" valign="bottom" rowspan="1" colspan="1">PIK3CA mutations, No.</th><th align="center" valign="bottom" rowspan="1" colspan="1">TP53 mutations, No.</th></tr></thead><tbody><tr><td rowspan="1" colspan="1">Luminal (ER-positive/HER2-negative)</td><td align="center" rowspan="1" colspan="1">126/410 (30.7%) <italic>P</italic> < .001</td><td align="center" rowspan="1" colspan="1">30/409 (7.3%)</td></tr><tr><td rowspan="1" colspan="1">HER2-positive</td><td align="center" rowspan="1" colspan="1">34/157 (21.7%)</td><td align="center" rowspan="1" colspan="1">19/157 (12.1%)</td></tr><tr><td rowspan="1" colspan="1">ER-negative/HER2-negative</td><td align="center" rowspan="1" colspan="1">16/120 (13.3%)</td><td align="center" rowspan="1" colspan="1">21/120 (17.5%) <italic>P</italic> < .001</td></tr></tbody></table><table-wrap-foot><fn fn-type="other"><p>* <italic>P</italic> values were calculated using a two-sided χ<sup>2</sup> test. ER = estrogen receptor.</p></fn></table-wrap-foot></table-wrap></sec><sec id="s9"><title>Associations With Prognosis</title><p>In the whole cohort that was genotyped, <italic>PIK3CA</italic> mutations were not statistically significantly associated with prognosis (DDFS: HR = 0.88 [95% CI = 0.58 to 1.34], <italic>P</italic> = .56; OS: HR = 0.603 [95% CI = 0.32 to 1.13], <italic>P</italic> = .11; <xref ref-type="fig" rid="F2">Figure 2</xref>). However, we noted that there was a statistically significant nonproportional prognostic effect over time for DDFS (<italic>P</italic> = .002) and RFS (<italic>P</italic> = .007) but not for OS (<italic>P</italic> = .12). An exploratory subdivision of the time axis at three years shows a favorable prognostic effect before three years (DDFS: HR = 0.57 [95% CI = 0.31 to 1.03], <italic>P</italic> = .06; RFS: HR = 0.55 [95% CI = 0.31 to 0.98], <italic>P</italic> = .04, respectively, and statistically nonsignificant effect after 3 years: DDFS: HR = 1.69 [95% CI = 0.90 to 3.16], <italic>P</italic> = .10; RFS: HR = 1.58 [95% CI = 0.86 to 2.88], <italic>P</italic> = .14).</p><fig fig-type="figure" id="F2" position="float"><label>Figure 2.</label><caption><p>Prognostic associations between patients who had a <italic>PIK3CA</italic> mutation (mt) vs wild type (WT) and clinical outcome. <bold>A–C)</bold> Kaplan-Meier plots of the cumulative proportion of patients surviving with the time in years. Various clinical end points are shown: distant disease-free survival (A), recurrence-free survival (B), and overall survival (C). Cox regression hazard ratios (HRs) and 95% confidence intervals (CIs) are shown, stratified by chemotherapy type given. All statistical tests are two-sided. The number of patients at risk in each group is given below the graphs.</p></caption><graphic xlink:href="jnci.j_djt121_f0002"></graphic></fig><p content-type="indent">No statistically significant differences in patient outcome were observed when <italic>PIK3CA</italic> mutations were evaluated separately according to their location (<xref ref-type="fig" rid="F3">Figure 3</xref>). Patients whose tumors contained a <italic>PIK3CA</italic> mutation were also not found to have a statistically significantly different survival than those with wild type in any of the breast cancer subtypes (<ext-link ext-link-type="uri" xlink:href="http://jnci.oxfordjournals.org/lookup/suppl/doi:10.1093/jnci/djt121/-/DC1">Supplementary Figure 1</ext-link>, available online).</p><fig fig-type="figure" id="F3" position="float"><label>Figure 3.</label><caption><p>Prognostic associations between <italic>PIK3CA</italic> genotype and clinical outcome according to mutation location on the gene (helical/exon 9 vs kinase/exon 20 domain). The number of patients at risk in each group is given below the graphs. <bold>A–C)</bold> Kaplan-Meier plots of the cumulative proportion of patients surviving with the time in years. Various clinical end points are shown: distant disease-free survival (A), recurrence-free survival (B), and overall survival (C). The two patients with dual mutations were excluded and all treatment arms were pooled. <italic>P</italic> values correspond to log-rank tests; mt = mutant; WT = wild type. All statistical tests are two-sided.</p></caption><graphic xlink:href="jnci.j_djt121_f0003"></graphic></fig><p content-type="indent"><italic>TP53</italic> mutations were not statistically significantly associated with prognosis in the whole genotyped cohort (DDFS log-rank <italic>P</italic> = .36; RFS <italic>P</italic> = .34; OS <italic>P</italic> = .11). Of the three <italic>ERBB2</italic> mutated tumors, one patient had a distant relapse and died of her disease.</p></sec><sec id="s10"><title>Association Between <bold><italic>PIK3CA</italic></bold> Mutations and Trastuzumab Efficacy in HER2-Positive Breast Cancer</title><p>We subsequently evaluated the association between <italic>PIK3CA</italic> genotype and trastuzumab efficacy in the HER2-positive population, considering preclinical data suggesting that <italic>PIK3CA</italic> mutations contribute to trastuzumab resistance (<xref rid="CIT0013" ref-type="bibr">13</xref>,<xref rid="CIT0015" ref-type="bibr">15</xref>). We found that in our dataset, the magnitude of trastuzumab benefit (with cytotoxic chemotherapy) did not differ statistically significantly according to <italic>PIK3CA</italic> genotype (<xref ref-type="fig" rid="F4">Figure 4</xref>; <italic>P</italic><sub>interaction</sub>: DDFS <italic>P</italic> = .14; RFS <italic>P</italic> = .17; OS <italic>P</italic> = .24). For the primary endpoint of DDFS and trastuzumab benefit, patients who were <italic>PIK3CA</italic> mutant had an HR of 0.19 (95% CI = 0.04 to 1.04; <italic>P</italic> = .06) vs patients who were <italic>PIK3CA</italic> wild type (HR = 0.98 [95% CI = 0.47 to 2.8], <italic>P</italic> = .97).</p><fig fig-type="figure" id="F4" position="float"><label>Figure 4.</label><caption><p>Interaction between <italic>PIK3CA</italic> genotype and trastuzumab efficacy. <bold>A)</bold> Kaplan-Meier plots comparing trastuzumab vs no trastuzumab treatment arms for <italic>PIK3CA</italic> mutated (mt), HER2-positive cohorts. Cumulative proportions of patients surviving distant disease free are shown. <bold>B)</bold> Kaplan-Meier plots comparing trastuzumab vs no trastuzumab for <italic>PIK3CA</italic> wild-type (WT), HER2-positive cohorts. Cumulative proportions of patients surviving distant disease free are shown. <bold>C)</bold> Interaction forest plots indicate Cox regression hazard ratios (HRs) and 95% confidence intervals (CIs) stratified by chemotherapy type given for trastuzumab benefit for distant disease-free survival (DDFS). according to <italic>PIK3CA</italic> genotype and by overall series. <bold>D)</bold> Kaplan-Meier plots comparing trastuzumab vs no trastuzumab treatment arms for <italic>PIK3CA</italic> mt, HER2-positive cohorts. Cumulative proportions of patients surviving relapse free are shown. <bold>E)</bold> Kaplan-Meier plots comparing trastuzumab vs no trastuzumab treatment arms for <italic>PIK3CA</italic> WT, HER2-positive cohorts. Cumulative proportions of patients surviving relapse free are shown. <bold>F)</bold> Interaction forest plots indicate Cox regression HRs and 95% CIs stratified by chemotherapy type given for trastuzumab benefit for recurrence-free survival (RFS) according to <italic>PIK3CA</italic> genotype and by overall series. <bold>G)</bold> Kaplan-Meier plots comparing trastuzumab vs no trastuzumab treatment arms for <italic>PIK3CA</italic> mt, HER2-positive cohorts. Cumulative proportions of patients alive are shown. <bold>H)</bold> Kaplan-Meier plots comparing trastuzumab vs no trastuzumab treatment arms for <italic>PIK3CA</italic> WT, HER2-positive cohorts. Cumulative proportions of patients alive are shown. <bold>I)</bold> Interaction forest plots indicate Cox regression HRs and 95% CIs stratified by chemotherapy type given for trastuzumab benefit for overall survival. according to <italic>PIK3CA</italic> genotype and by overall series. All statistical tests are two-sided. The number of patients at risk in each group is given below the graphs.</p></caption><graphic xlink:href="jnci.j_djt121_f0004"></graphic></fig></sec></sec><sec id="s11"><title>Discussion</title><p>The primary objective of this study was to investigate the clinical relevance of <italic>PIK3CA</italic> mutations with regard to prognosis and benefit from adjuvant trastuzumab. While confirming the dominance of <italic>PIK3CA</italic> and <italic>TP53</italic> mutations in breast cancer with few other known mutations being present in breast cancer at a high rate, we showed that <italic>PIK3CA</italic> mutations, regardless of location, were not statistically significantly associated with prognosis in breast cancer over the entire follow-up period, although, interestingly, there was a statistically significant nonproportional prognostic effect for DDFS and RFS over time. Initially, a better outcome for the mutant genotype compared with wild type was seen, consistent with the mutant genotype’s association with favorable clinicopathological features; however, this effect disappeared after three years. Perhaps this pattern can be explained by the high-risk population studied or reflect a biological tendency for long-term relapse, as endocrine therapy resistance could conceivably develop through enhanced PI3K signaling. In general, however, the prognostic direction in the first three years supports the results from many of the larger cohort series reported in the literature, even though the prognostic association has yielded conflicting reports overall (<xref rid="CIT0004" ref-type="bibr">4–7</xref>,<xref rid="CIT0029" ref-type="bibr">29</xref>).</p><p content-type="indent">The unique advantage and strength of our study was that we could evaluate interactions between <italic>PIK3CA</italic> mutations and trastuzumab benefit in the context of a randomized clinical trial in which patients with HER2-positive breast cancer received treatment with or without trastuzumab. To our knowledge, this study also represents the largest breast cancer cohort with clinical outcome data to be genotyped for <italic>PIK3CA</italic> and multiple other known cancer somatic mutations. Furthermore, we covered greater than 94% of known <italic>PIK3CA</italic> mutations, rather than limiting to hotspot areas. Preclinical data suggest that <italic>PIK3CA</italic> mutations could identify a subgroup of patients with HER2-positive disease resistant to trastuzumab, but our data do not support this. In fact, the <italic>PIK3CA</italic> mutant compared with wild-type, HER2-positive tumors seemed to derive more benefit from adjuvant trastuzumab, suggesting increased dependency on p110α, although the interaction test is not statistically significant (<xref rid="CIT0030" ref-type="bibr">30</xref>). All the patients in this study also received chemotherapy with trastuzumab, which is standard practice, so we cannot discount the possibility that mutations could cause resistance to trastuzumab as a single agent. It has been proposed that scheduling of chemotherapy either before or after administration of trastuzumab could affect clinical outcomes, particularly through immune mechanisms (<xref rid="CIT0031" ref-type="bibr">31</xref>). As the generation of antitumor immunity has been proposed as a dominant mechanism of action for the efficacy of trastuzumab, it is plausible that <italic>PIK3CA</italic> mutations could alter the immune microenvironment to be either or antitumor or protumor (<xref rid="CIT0031" ref-type="bibr">31</xref>,<xref rid="CIT0033" ref-type="bibr">33</xref>). PI3K signaling per se is known to affect immune signaling, although no data currently exist with regard to specific mutation-related events (<xref rid="CIT0034" ref-type="bibr">34</xref>). Therefore, despite <italic>PIK3CA</italic> mutations being oncogenic activators of the PI3K pathway, overall our data support the notion that <italic>PIK3CA</italic> mutant tumors when compared with the <italic>PIK3CA</italic> wild-type tumors are not resistant to standard adjuvant chemotherapy, trastuzumab, and endocrine therapy regimens.</p><p content-type="indent">A biological mechanism for these observations is currently unknown. We have speculated previously that <italic>PIK3CA</italic> mutations are not effective at completely activating the pathway and negative feedback mechanisms may serve to weaken the oncogenic signal (<xref rid="CIT0006" ref-type="bibr">6</xref>). Full AKT activation has not been associated with the mutation, and AKT-independent signaling has been proposed through PDK1-SGK3, with SGK3 also implicated with estrogen signaling (<xref rid="CIT0007" ref-type="bibr">7</xref>,<xref rid="CIT0035" ref-type="bibr">35–37</xref>). Estrogen has also been shown predominantly to repress transcription of many genes, which may also reduce the final signaling output (<xref rid="CIT0038" ref-type="bibr">38</xref>,<xref rid="CIT0039" ref-type="bibr">39</xref>). High levels of pathway activation could be detrimental for tumor growth (ie, result in senescence), analogous to PTEN deficiency (<xref rid="CIT0040" ref-type="bibr">40</xref>). Regardless, it seems that high levels of pathway activation are not associated with <italic>PIK3CA</italic> mutations per se. We hypothesize that <italic>PIK3CA</italic> mutations may be more important in breast cancer initiation and malignant transformation whereas other mutations may be required to drive the acquisition of aggressive biological features: it is notable that <italic>PIK3CA</italic> mutations often coexist with other lesions in the same pathway (<xref rid="CIT0030" ref-type="bibr">30</xref>,<xref rid="CIT0041" ref-type="bibr">41–43</xref>). It remains to be seen if primary and/or metastatic breast cancer patients with <italic>PIK3CA</italic> mutations will derive increased benefit from PI3K pathway–targeted drugs, which has been observed in vitro (<xref rid="CIT0044" ref-type="bibr">44–46</xref>). Many clinical trials evaluating potential benefit from specific PI3K targeted drugs are currently ongoing.</p><p content-type="indent">This study, as well as others using massively parallel sequencing, have confirmed that breast cancers contain a large number of known cancer driver mutations that occur infrequently (<xref rid="CIT0042" ref-type="bibr">42</xref>,<xref rid="CIT0043" ref-type="bibr">43</xref>,<xref rid="CIT0047" ref-type="bibr">47</xref>,<xref rid="CIT0048" ref-type="bibr">48</xref>). In this cohort we have identified three <italic>ERBB2</italic> as well as single <italic>KRAS, ALK, STK11/LKB1</italic>, and <italic>AKT2</italic> mutations. These are known “driver” mutations, yet it is unknown how these influence outcomes or are amenable to targeted therapies in breast cancer. <italic>ERRB2</italic> kinase domain mutations have recently been shown to be important in breast cancer; hence, this mutation could represent a new target for non-HER2-amplified/overexpressing breast cancer (<xref rid="CIT0049" ref-type="bibr">49–52</xref>).</p><p content-type="indent">To our knowledge, this is the only study thus far to address the relevance of <italic>PIK3CA</italic> genotype and trastuzumab benefit. We acknowledge several limitations of our study, specifically the low number of events in the HER2-positive subgroup, which does not exclude the possibility that an effect might be seen in a larger series; less than 100% coverage of all reported <italic>PIK3CA</italic> mutations in breast cancer; and sequencing from one tumor section, given emerging data on intratumoral heterogeneity (<xref rid="CIT0053" ref-type="bibr">53</xref>). Next-generation sequencing technologies may give us a more complete picture of the clonal composition and molecular landscape of these tumors. However, it is becoming clear that elucidating the relationship between infrequent but known driver genetic aberrations, prognosis, and drug response will require the genotyping of tumors from many thousands of breast cancer patients. This may also prove challenging for drug development. Nevertheless, our study provides important information from a large randomized clinical trial dataset about the prevalence and relationship between targetable and known somatic driver mutations, trastuzumab efficacy, and prognosis.</p></sec><sec><title>Funding</title><p>This study was funded by the <funding-source>Breast Cancer Research Foundation</funding-source>. The funding source had no role in the study design, data analysis, data collection, data interpretation or writing of the report. SL, SM, and CS are supported by the Breast Cancer Research Foundation. SL is supported by the <funding-source>Fonds JC Heuson Belgium</funding-source> This project was also supported by EU-FP7 project RESPONSIFY No 278659.</p></sec><sec sec-type="supplementary-material"><title>Supplementary Material</title><supplementary-material id="PMC_1" content-type="local-data"><caption><title>Supplementary Data</title></caption><media mimetype="text" mime-subtype="html" xlink:href="supp_105_13_960__index.html"></media><media xlink:role="associated-file" mimetype="application" mime-subtype="pdf" xlink:href="supp_djt121_jnci_JNCI_12_1745_s01.pdf"></media></supplementary-material></sec></body><back><notes><p>PB previously received payment for a lecture from Roche Finland. The other authors report no conflicts of interest. No medical writing assistance was used for this manuscript. 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