Chemotherapy In Luminal Breast Cancer Research Articles

Abstract PO5-02-02: LyKi1: a highly predictive immune-based score of pathological response to chemotherapy in luminal breast cancer

Abstract Background: Identifying which luminal breast cancer (BC) patients will benefit from (neo)adjuvant chemotherapy still remains a challenge. Efficient predictive tests and genomic signatures are still lacking. Because neoadjuvant chemotherapy (NACT) constitutes an in vivo chemotherapy-sensitivity test, we used the pre-treatment core biopsies to identify predictive factors of pathological response in luminal HR+/Her2- BC. Analyses on sTILS and TIL subtypes (CD3, CD4, CD8, CD20) are rare in luminal BC while a predictive value has been reported in TNBC and Her-2 positive BC. Patients and Methods: We performed a retrospective analysis of 211 patients treated with NACT followed by surgery for a T0-T4 luminal (HR+/Her-2-) breast cancer. Association of anthracyclin and taxane was used in 99% of cases. Median age was 48.4 yo (28 – 76). Initial clinical stage was: I: 0.5%, II: 67%; III: 33%. Biopsy analyses were the followings: NST carcinomas: 90%; luminal B: 82%; E&E grade I/II/III: 10%/57%/32%; KI-67 > 20%: 74%. After NACT, RCB 0-1 was observed in 32 cases (15.2%) and pCR in 19 (9.0 %) cases. All pre-therapeutic biopsies were reviewed for pathological factors, blinded to the patients’ outcomes. Clinico-pathological analyses were performed using standard methods. sTILs were estimated on H&E slides. Intratumoral CD3, CD4, CD8 and CD20 infiltrates were carried out by an independent laboratory using a machine learning model for automated and quantitative evaluation based on digital IHC stains. Correlation between sTILs and TIL subtypes were assessed by Spearman’s coefficient. Predictive factors of RCB 0/1 were explored using unconditional logistic regression analysis. After selection on univariate analysis (p< 0.05), multivariate prognostic models were developed using stepwise backward variable elimination process and compared using area under the curve (AUC). Results: sTILs levels ≥ 10% were observed in 15.2% of biopsies. 42 % of tumors had ≥ 10 % CD3-positive cells, 46% for CD4, 38% for CD8, and 8% for CD20. A high level of correlation was observed between these different markers (Spearman’s coefficient ≥ 0.73 for each comparison). By univariate analysis, significant factors (p< 0.05) associated with RCB 0-1 were: age, mitotic index (1 vs 2 vs 3), aneuploidy (1-2 vs 3), differentiation (1-2 vs 3), E&E grade (1-2 vs 3), mitotic count, Ki-67, Magee equation 3, phenotypic group (luminal A vs B), ER, sTILs and each TIL subtype. Age, E&E grade, Ki-67, Magee equation 3, sTILs and all TIL subtypes were selected for multivariate analysis. As TIL subtypes were highly correlated, different predictive models could be performed. The model we selected (called LyKi1) was constructed based on CD8 [OR 2.24 (95%CI 1.55-3.25; p< 10-4)], E&E grade [OR 3.00 (95%CI 1.22-7.42; p=0.017)] and Ki-67 [OR 1.74 (95%CI 1.09-2.76; p=0.019)]. It was highly significantly associated with RCB 0-1, with an AUC value of 0.856 (95%CI 0.756-0.916). For instance, LyKi1 could isolate a group of 30% of patients that achieved RCB 0-1 in 39.7% of cases vs 4.7% for the 70% of patients with lowest LyKi1 scores. pCR rates were 25% and 2%, respectively. Importantly, prediction value of LyKi1 remained very high if restricted to luminal B tumors (AUC 0.817; 95% CI: 0.741-0.894). Ability to predict pCR was also very high (AUC 0.837 (95%CI: 0.750-0.924) in this phenotype). Conclusion: This retrospective analysis clearly highlights the important predictive role of sTILs and TIL subpopulations for the response to an anthracyclin-taxane based regimen in a neoadjuvant setting for luminal breast cancer. Within this cohort, LyKi1 score (combining CD8, grade and Ki-67) has a very high value to predict pathological response to NACT in luminal breast cancer. Importantly, such score has to be validated in a multicentric prospective cohort and investigated in adjuvant setting. Citation Format: Marc Debled, Hugo Deboissy, Coralie Cantarel, Chloé Delfour, Léonie Alran, Marion Fournier, Nathalie Quenel-Tueux, Valérie Velasco, Foucault Chammings, Véronique Brouste, Monica Arnedos, Gaétan MacGrogan. LyKi1: a highly predictive immune-based score of pathological response to chemotherapy in luminal breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-02-02.

Abstract P2-12-10: Improving pathological complete response in luminal breast cancer after neoadjuvant chemotherapy

Abstract Pathological complete response to neoadjuvant chemotherapy in luminal breast cancer is extremely low so its role is very controversial. We investigated whether the use Oncotype DX recurrence score ® could improve this response.We performed the Oncotype Dx in 103 patients with early breast cancer with high-risk clinical criteria to receive neoadjuvant chemotherapy. We reported the pathologic complete response (pCR) and its association with Oncotype DX.Median age was of 51 (24-87), median tumor size of 26 mm and 51 (57%) patients had initial node involvement. Median estrogen and progesterone receptor by histoscore was 280 and 40 and median Ki67 value was 30. 50 patients were less than 50 years old. Median Oncotype score was 30 (12-76) and 15 (14,6%) in the 11-18 score interval, 21 (20,4%) in the 18-25 and 67 (65%) with a score greater than 25. 24 (23,3%) patients obtained pCR, 22% in initial node involvement and 25% in not nodal involvement.The Oncotype DX its highly correlated with pCR (OD: 1,059, p: 0,004) in the multivariable analysis and in patients with Oncotype DX RS≥25 there was a strong correlation (OD: 18,295, P:0,005). pCR according with age (less or more than 50) and Oncotype Dx interval score is reported in the table. Conclusion: Oncotype DX recurrence score ® predicts with great accuracy the pathologic complete response. Patients with RS 11-25 had a very low pCR, whereas patients with RS>25 achieve a 34% of pCR. Based on initial considered High risk Score (>31) we achieve a total of 18 to 42 patients of pCR (42%). Oncotype DX recurrence score ® allows a very considerable increase in the complete pathological responses after neoadjuvant chemotherapy, so it should be considered when selecting the patients who should receive neoadjuvant chemotherapy. LESS THAN 50 YEARSMORE THAN 50 YEARSONCOTYPETOTALpCRTOTALpCRRS 11-18100 (0%)50 (0%)RS 18-25101 (10%)110 (0%)RS > 253011(36%)3712 (32%) Citation Format: Ariadna Gasol Cudós, Serafin Morales Murillo, Alvaro Rodriguez Galindo, Ona Pallisé Subirats, Jordi Melé Olivé, Carles Canosa Morales, Felip vilardell villellas, Douglas René Sanchez Guzman, Joel Veas Rodriguez. Improving pathological complete response in luminal breast cancer after neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-12-10.

Stratification of Prognosis by Biological Features Following Neoadjuvant Chemotherapy in Luminal Breast Cancer.

There are few models predicting breast cancer prognosis among patients receiving neoadjuvant chemotherapy (NAC) for estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative (luminal) breast cancer. We examined whether biological features (BFs) of residual tumors are prognostic factors following NAC. We enrolled patients with remnant tumors following NAC for luminal breast cancer and evaluated clinical stage, pathological stage, BFs prior to NAC, and BFs following NAC as prognostic factors. BFs were divided into high and low risk using the previously reported YR-IHC4 model calculated according to ER, progesterone receptor (PgR), HER2, and the proliferation marker Ki-67. A total of 57 patients were enrolled in the current study. We observed a statistically significant difference in relapse-free survival (RFS) between the BF risk categories via YR-IHC4 predictions following NAC (p=0.044). The 5-year RFS rates of the BF low- and high-risk groups following NAC were 84.2% and 52.5%, respectively. BFs of residual tumors following NAC may be important prognostic factors in luminal breast cancer.

Selection of neoadjuvant treatment based on the 21-GENE test results in luminal breast cancer

Neoadjuvant chemotherapy (NAC) is an optimal option in early breast cancer, but in ER-positive/HER2-negative (luminal) is still controversial, although a survival benefit has recently been observed when a histological response by Symmans’ method type 0 or I is achieved. The 21-gene Oncotype DX Breast Recurrence Score® assay (Oncotype DX®) is a validated test to assess the survival benefit of adjuvant chemotherapy in these patients but its role in the neoadjuvant setting is less established. We analyzed the results of the Oncotype DX® test in a cohort of 122 consecutive patients selected to receive NAC based on classical clinicopathological parameters and the correlation between the Oncotype DX® results and the pathological response assessed by Symmans’ method. Median age was 56.5 (range 31–84) years. Initial tumor size was T1 (<20 mm) in 46 patients (37.7%), 57 (46.7%) had a T2 tumor (20–50 mm), and 19 (15.6%) had a tumor size more than 50 mm. 59 (48.4%) had axillary node involvement. The median expression estrogen and progesteron receptors by immunohistochemistry was 280 and 120 respectively and median Ki67 index was 28%. The Recurrence Score (RS) results were <11 in 21 patients (17.2%) patients, RS 11 to 25 in 58 (47.5%), and RS > 25 in 43 (35.2%). Considering the Oncotype DX test results, neoadjuvant chemotherapy was administered to 60 patients (49%), 11 (9%) received adjuvant chemotherapy and 51 (42%) no chemotherapy. Testing with the assay has therefore led to 42% fewer chemotherapy treatments. Among 60 patients receiving NAC, pathologic response was achieved for 5 patients (8.3%) with RCB-0 and 15 RCB-1 (25%). We did not find any pathological response RCB-0 and RCB-I in the 20 patients who received NAC and had a Recurrence Score result <21 for the premenopausal group, or a RS result <25 for the postmenopausal group. For patients with highest Recurrence Score results (RS > 21 or 25 according to menopausal status) it was 12% (5/40) RCB-0 and 40% (16/40) RCB-I. ConclusionsThe Oncotype DX test could be a useful tool to select patients candidates for neoadjuvant chemotherapy in luminal breast cancer. Neoadjuvant chemotherapy could be avoided in 42% of patients. We found a correlation between Recurrence Score results and pathological response with 14% of RCB-0 and a total of 47% of significant pathological response type RCB-0 and RCB-I in patients with highest Recurrence Score results. Interestingly, patients with a Recurrence Score result inferior to 32 did not get any histological response type 0 and only 5% RCB-I.

Association of Menopausal Status, Expression of Progesterone Receptor and Ki67 to the Clinical Response to Neoadjuvant Chemotherapy in Luminal Breast Cancer.

To identify the biomarkers of response to neoadjuvant chemotherapy in early luminal breast cancer. A cross-sectional study that included all patients with early or locally-advanced luminal breast cancer submitted to neoadjuvant chemotherapy between 2013 and 2014. Demographic, clinic and pathologic data were retrieved from patient records. The expressions of the estrogen receptor (ER), the progesterone receptor (PR), and Ki67 were analyzed by immunohistochemistry (IHC). The status of the human epidermal growth factor receptor 2 (HER2) was evaluated by IHC and fluorescent in situ hybridization (FISH). Independent predictors of clinic and pathologic response were evaluated by stepwise logistic regression models and receiver operating characteristic (ROC) curve analysis. Out of 298 patients identified, 115 were included in the analysis. Clinical complete response (cCR) was observed in 43.4% of the patients (49/113), and pathologic complete response (pCR) was observed in 7.1% (8/115) of the patients. The independent predictors of cCR were premenopausal status (p < 0.001), low PR expression (≤ 50% versus > 50%; p = 0.048), and Ki67 expression ≥ 14% (versus < 14%; p = 0.01). Patients with cCR were more commonly submitted to breast conserving surgery (34.7% versus 7.8%; p < 0.001). Increasing cut-off points for Ki67 expression were associated with an increase in specificity and a decrease in sensitivity to identify patients with cCR. Premenopausal status, lower PR expression and higher Ki67 expression were associated with a higher rate of cCR to neoadjuvant chemotherapy in luminal breast cancer.

ROCplot.org: Validating predictive biomarkers of chemotherapy/hormonal therapy/anti-HER2 therapy using transcriptomic data of 3,104 breast cancer patients.

Systemic therapy of breast cancer can include chemotherapy, hormonal therapy and targeted therapy. Prognostic biomarkers are able to predict survival and predictive biomarkers are able to predict therapy response. In this report, we describe the initial release of the first available online tool able to identify gene expression-based predictive biomarkers using transcriptomic data of a large set of breast cancer patients. Published gene expression data of 36 publicly available datasets were integrated with treatment data into a unified database. Response to therapy was determined using either author-reported pathological complete response data (n = 1,775) or relapse-free survival status at 5 years (n = 1,329). Treatment data includes chemotherapy (n = 2,108), endocrine therapy (n = 971) and anti-human epidermal growth factor receptor 2 (HER2) therapy (n = 267). The transcriptomic database includes 20,089 unique genes and 54,675 probe sets. Gene expression and therapy response are compared using receiver operating characteristics and Mann-Whitney tests. We demonstrate the utility of the pipeline by cross-validating 23 paclitaxel resistance-associated genes in different molecular subtypes of breast cancer. An additional set of established biomarkers including TP53 for chemotherapy in Luminal breast cancer (p = 1.01E-19, AUC = 0.769), HER2 for trastuzumab therapy (p = 8.4E-04, AUC = 0.629) and PGR for hormonal therapy (p = 8.6E-05, AUC = 0.7), are also endorsed. The tool is designed to validate and rank new predictive biomarker candidates in real time. By analyzing the selected genes in a large set of independent patients, one can select the most robust candidates and quickly eliminate those that are most likely to fail in a clinical setting. The analysis tool is accessible at www.rocplot.org.

Abstract P5-12-12: ROCplot.org: validating predictive biomarkers of response to chemotherapy/hormonal therapy/anti-HER2 therapy using transcriptomic data of 3,150 breast cancer patients

Abstract Prognostic biomarkers are capable to predict survival and predictive biomarkers are capable to predict therapy response. Systemic therapy of breast cancer can include chemotherapy, hormonal therapy, and targeted therapy. Here, we report the initial release of the first online available tool capable to identify gene expression based predictive biomarkers using transcriptomic data of a large set of breast cancer patients. Published gene expression data of 44 publicly available datasets was integrated with treatment data into a unified database. The classification is based on either author-reported pathological complete response (n=1,775) or relapse-free survival status at five years (n=1,329). Treatment data includes chemotherapy (n=2,108), endocrine therapy (n=971), and anti-HER2 therapy (n=267). The transcriptomic database includes 20,089 unique genes and 54,675 probe sets. Gene expression and therapy response are compared using receiver operating characteristics and Mann-Whitney tests. We demonstrate the utility of the tool by validating a set of established biomarkers including TP53 for chemotherapy in luminal breast cancer (p=5.2e-20, AUC=0.77), ERBB2 for trastuzumab therapy (p=8.4e-04, AUC=0.629), and PGR for hormonal therapy (p=8.6e-05, AUC=0.7). The tool is designed to validate and rank new predictive biomarker candidates in real time. By analyzing the selected genes in a large set of independent patients, one can select the most robust candidate and quickly eliminate those which are most likely to fail in a clinical setting. The analysis tool is accessible at www.rocplot.org/breast. Citation Format: Győrffy B, Fekete J. ROCplot.org: validating predictive biomarkers of response to chemotherapy/hormonal therapy/anti-HER2 therapy using transcriptomic data of 3,150 breast cancer patients [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-12-12.

Risk Assessment after Neoadjuvant Chemotherapy in Luminal Breast Cancer Using a Clinicomolecular Predictor.

Purpose: This study aimed to evaluate a modified EPclin test (mEPclin), a combination of EndoPredict (EP) score, post-neoadjuvant pathologic tumor size and nodal status, for predicting the risk of distance recurrence after neoadjuvant chemotherapy (NACT) in patients with residual estrogen receptor (ER)-positive/HER2-negative breast cancer. We also compared the prognostic power of the mEPclin with that of the CPS-EG score.Experimental Design: A total of 428 formalin-fixed, paraffin-embedded tumor samples from GeparTrio and GeparQuattro studies were evaluated for mRNA expression of eight cancer-related and three reference genes. The mEPclin score was computed using a modified algorithm and predefined cut-off values were used to classify each patient at low or high risk. Primary endpoint was disease-free survival (DFS).Results: A higher continuous mEPclin score was significantly associated with increased risk of relapse [HR, 2.16; 95% confidence interval (CI), 1.86-2.51; P < 0.001] and death (HR, 2.28; 95% CI, 1.90-2.75; P < 0.001). Similarly, patients classified at high risk by dichotomous mEPclin showed significantly poorer DFS and overall survival compared with those at low risk. In contrast with CPS-EG, the mEPclin remained significantly prognostic for DFS in multivariate analysis (HR, 2.13; 95% CI, 1.73-2.63; P < 0.001). Combining CPS-EG and other clinicopathological variables with mEPclin yielded a significant improvement of the prognostic power for DFS versus without mEPclin (c-indices: 0.748 vs. 0.660; P < 0.001).Conclusions: The mEPclin score independently predicted the risk of distance recurrence and provided additional prognostic information to the CPS-EG score to assess more accurately the prognosis after NACT in the luminal non-pCR patient population. Therefore, this approach can be used to select patients for additional post-neoadjuvant therapies. Clin Cancer Res; 24(14); 3358-65. ©2018 AACR.

Risk assessment after neoadjuvant chemotherapy in luminal breast cancer: A prospectively planned validation of gene expression based and clinical prognostic scores in 428 residual tumor samples from two neoadjuvant clinical trials.

522Background: Gene expression assays are well-established prognostic tools for planning of adjuvant therapy in luminal breast cancer (BC). In the neoadjuvant setting, pCR is not an optimal prognos...

1944 Pathological complete response (pCR) after neoadjuvant chemotherapy in luminal breast cancer

1944 Pathological complete response (pCR) after neoadjuvant chemotherapy in luminal breast cancer

P5-13-15: Proteomic Identification of Predictive Biomarkers of Resistance to Neoadjuvant Chemotherapy in Luminal Breast Cancer: A Possible Role for 14–3-3 and BID?

Abstract Background: Chemotherapy resistance is a major obstacle in effective neoadjuvant treatment for oestrogen receptor (ER)-positive breast cancer. The ability to predict tumour response would allow chemotherapy administration to be directed towards only those patients who would benefit, thus maximising treatment efficiency. We aimed to identify protein biomarkers associated with chemotherapy resistance, using proteomic analysis of fresh ER-positive breast cancer samples, and then to perform pilot clinical validation experiments. Materials and Methods: Chemotherapy resistant and chemotherapy sensitive tumour samples were collected from breast cancer patients who received standard anthracycline-based neoadjuvant therapy consisting of epirubicin with cyclophosphamide followed by docetaxel. Comparative proteomics experiments were performed using invasive ductal carcinomas which demonstrated ER-positivity (luminal subtype). Protein expression was compared between chemotherapy resistant and chemotherapy sensitive tumour samples using 2-dimensional gel electrophoresis (2-DE) with MALDI-TOF/TOF mass spectrometry (MS). In addition the Panorama XPRESS Profiler725 antibody microarray, containing 725 antibodies from a wide variety of cell signalling and apoptosis pathways, was employed in the discovery phase. Differentially expressed proteins (DEPs) were submitted to Ingenuity Pathway Analysis (IPA) to identify any canonical pathway links. A pilot series of archival breast cancer samples, from patients treated with neoadjuvant anthracycline-based chemotherapy, was used for preliminary clinical validation of putative predictive biomarkers. Results: Five datasets were generated by antibody microarray analysis, revealing 41 targets. Of these, 7 DEPs were identified in at least 2 datasets and these included 14–3-3, BID and Bcl-xL. The top canonical pathway matched in IPA was “ERK5 signaling”, which involved 6 DEPs, including 14–3-3. The “PI3/AKT” pathway also involved 6 DEPs, including 14–3-3 and Bcl-xL. Three datasets were generated using 2-DE with MALDI-TOF/TOF MS, containing over 300 DEPs. These included several isoforms of 14–3-3. Differential expression of 14–3-3, BID and Bcl-xL was confirmed by immunoblotting in samples used for the discovery phase. A pilot clinical validation using immunohistochemical analysis of archival breast cancers revealed 14–3-3 tau and tBID to be significantly associated with chemotherapy resistance. Discussion: We have successfully utilised clinical tumour samples for the discovery of putative biomarkers of chemotherapy resistance using two complementary proteomic platforms. We propose a potential role for 14–3-3 tau and BID as predictive biomarkers of chemotherapy resistance in ER-positive tumours and further validation in a larger sample series is now required. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-13-15.

Proteomic identification of predictive biomarkers of resistance to neoadjuvant chemotherapy in luminal breast cancer: A possible role for 14-3-3 theta/tau and tBID?

Chemotherapy resistance is a major obstacle in effective neoadjuvant treatment for estrogen receptor-positive breast cancer. The ability to predict tumour response would allow chemotherapy administration to be directed towards only those patients who would benefit, thus maximising treatment efficiency. We aimed to identify putative protein biomarkers associated with chemotherapy resistance, using fresh tumour samples with antibody microarray analysis and then to perform pilot clinical validation experiments. Chemotherapy resistant and chemotherapy sensitive tumour samples were collected from breast cancer patients who had received anthracycline based neoadjuvant therapy consisting of epirubicin with cyclophosphamide followed by docetaxel. A total of 5 comparative proteomics experiments were performed using invasive ductal carcinomas which demonstrated estrogen receptor positivity (luminal subtype). Protein expression was compared between chemotherapy resistant and chemotherapy sensitive tumour samples using the Panorama XPRESS Profiler725 antibody microarray containing 725 antibodies from a wide variety of cell signalling and apoptosis pathways. A pilot series of archival samples was used for clinical validation of putative predictive biomarkers. AbMA analysis revealed 38 differentially expressed proteins which demonstrated at least 1.8 fold difference in expression in chemotherapy resistant tumours and 7 of these proteins (Zyxin, 14-3-3 theta/tau, tBID, Pinin, Bcl-xL, RIP and MyD88) were found in at least 2 experiments. Clinical validation in a pilot series of archival samples revealed 14-3-3 theta/tau and tBID to be significantly associated with chemotherapy resistance. For the first time, antibody microarrays have been used to identify proteins associated with chemotherapy resistance using fresh breast cancer tissue. We propose a potential role for 14-3-3 theta/tau and tBID as predictive biomarkers of neoadjuvant chemotherapy resistance in breast cancer. Further validation in a larger sample series is now required.

Validated 3 Gene Signature Predicts Response to Neo-Adjuvant Chemotherapy in Luminal Breast Cancer – Results from GeparTrio and GeparQuattro.

Abstract Background:Luminal breast cancers (LBC), defined as positive for estrogen receptor (ER) and negative for HER2, are of variable prognosis. We hypothesize that those showing potential sensitivity to neoadjuvant chemotherapy (NACT) are those with an unfavorable prognosis unless they achieve a pathological complete response (pCR = no invasive residual in breast and nodes). We therefore aimed to develop a Neoadjuvant Luminal Response Score (NLRS), identifying luminal tumors with a high chance for a pCR.Patients and methods:We examined mRNA expression of 57 candidate genes and 3 normalization genes by kinetic RT-PCR in FFPE tissues from pre-therapeutic core biopsies from 380 patients with LBC and cT2-T4 disease either receiving NACT with 6x docetaxel/ doxorubicin/ cyclophosphamide in GeparTrio (n=148, training cohort) or epirubicin/ cyclophosphamide followed by Docetaxel (+simultaneous or sequential capecitabine) in GeparQuattro (n=232, validation cohort). Samples were classified as LBC by mRNA expression analysis of ESR1 and Her2 using pre-defined cut-off values. The continuous NLRS was generated by linear combination of expression values of 3 out of 57 genes.The first pre-specified primary objective of training part was to demonstrate that the percentage of patients with LBC with a pCR is higher in NLRS+ tumors than in all LBC. The second pre-specified primary objective for validation part was to demonstrate that the NLRS score is able to identify the pCR cases in the LBC with a sensitivity of ≥80% (upper confidence interval [CI] limit) and a positive predictive value (PPV) of ≥20%.Results:In the GeparTrio training cohort, the NLRS was predictive of pCR by logistic regression analysis (AUC=0.88). Using a cutoff defined on the basis of the ROC curve, the NLRS was able to predict pCR with a sensitivity of 82%, a specificity of 77%. While the pCR rate in all LBC was only 8.2%, this rate increased to 24% in the group of 57 patients with a NLRS above the cutoff. The pCR rate in 91 patients with a NLRS below the cutoff was 1.1%.When patients of all 3 treatment arms of the GeparQuattro validation cohort were analyzed together, AUC was 0.69 (p=0.004). Using the pre-defined cutoff, sensitivity was 52% (95% CI: 33-71%), specificity 74% (69-79%) and PPV 16%. However, if only patients treated without capecitabine containing NACT (to be more comparable to GeparTrio) (N=68) were included, AUC was 0.72 and sensitivity increased to 63 (23-89)% and PPV to 21%, passing the pre-defined limits for positive validation.Conclusion:Our study provides proof-of-principle that it is possible to develop and validate predictive signatures for response to NACT based on pretherapeutic FFPE biopsies. The 3-gene NLRS is a first validated gene signature selecting LBC with a high chance for pCR to NACT. The results are a basis for further development of predictive signatures. A prospective evaluation will be performed in the PREDICT study, a substudy to GeparQuinto.Supported by a BMBF grant. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 2132.