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Purpose:The Breast Cancer Index (BCI) HOXB13/IL17BR (H/I) ratio predicts benefit from extended endocrine therapy in hormone receptor–positive (HR+) early-stage breast cancer. Here, we report the final analysis of the Trans-aTTom study examining BCI (H/I)'s predictive performance.Experimental Design:BCI results were available for 2,445 aTTom trial patients. The primary endpoint of recurrence-free interval (RFI) and secondary endpoints of disease-free interval (DFI) and disease-free survival (DFS) were examined using Cox proportional hazards regression and log-rank test.Results:Final analysis of the overall study population (N = 2,445) did not show a significant improvement in RFI with extended tamoxifen [HR, 0.90; 95% confidence interval (CI), 0.69–1.16; P = 0.401]. Both the overall study population and N0 group were underpowered due to the low event rate in the N0 group. In a pre-planned analysis of the N+ subset (N = 789), BCI (H/I)-High patients derived significant benefit from extended tamoxifen (9.7% absolute benefit: HR, 0.33; 95% CI, 0.14–0.75; P = 0.016), whereas BCI (H/I)-Low patients did not (−1.2% absolute benefit; HR, 1.11; 95% CI, 0.76–1.64; P = 0.581). A significant treatment-to-biomarker interaction was demonstrated on the basis of RFI, DFI, and DFS (P = 0.037, 0.040, and 0.025, respectively). BCI (H/I)-High patients remained predictive of benefit from extended tamoxifen in the N+/HER2− subgroup (9.4% absolute benefit: HR, 0.35; 95% CI, 0.15–0.81; P = 0.047). A three-way interaction evaluating BCI (H/I), treatment, and HER2 status was not statistically significant (P = 0.849).Conclusions:Novel findings demonstrate that BCI (H/I) significantly predicts benefit from extended tamoxifen in HR+ N+ patients with HER2− disease. Moreover, BCI (H/I) demonstrates significant treatment to biomarker interaction across survival outcomes.

Supplementary Figure from Breast Cancer Index Is a Predictive Biomarker of Treatment Benefit and Outcome from Extended Tamoxifen Therapy: Final Analysis of the Trans-aTTom Study

Background: Several biomarkers such as estrogen receptor (ER), progesterone receptor (PR), androgen receptor (AR) and Ki67 have been implicated in the pathogenesis and/or as prognostic biomarkers of breast cancer, and are utilized to determine treatment. Given the heterogeneity of response to endocrine therapy, however, predictive biomarkers are critical to better individualize patient care. Previous results from the Trans-aTTom study demonstrated that the Breast Cancer Index HOXB13/IL17BR [BCI (H/I)] biomarker significantly predicts extended endocrine benefit from 10 vs 5y of tamoxifen. In this correlative study, the predictive activity of BCI (H/I) was compared with ER, PR, AR and Ki67 protein expression in node positive patients treated in the aTTom trial. Methods: Patients with available tumor tissue and biomarker analyses were included. ER, PR, AR and Ki67 were centrally assessed by immunohistochemistry (IHC) utilizing tissue microarrays. BCI gene expression analysis by RT-PCR was performed blinded to clinical outcome. Multivariate Cox models adjusting for age, tumor size, tumor grade and HER2 status were used to assess the significance of the interaction between treatment and each biomarker as continuous variables. 17-year risk of recurrence, as a function of each continuous biomarker, was estimated from Cox models in each of the 2 treatment arms. Results: Analysis of 789 HR+, N+ patients showed a weak negative correlation between BCI (H/I) and ER, PR, and AR expression whereas Ki67 and the AR/ER ratio showed no correlation (ER, cor=−0.18; PR, cor=−0.25; AR, cor=−0.14; Ki67, cor=0.04; AR/ER ratio, cor=0.02). The interaction between BCI (H/I) and extended tamoxifen treatment was significant (p=0.014). In addition, analysis of risk of recurrence as a function of continuous BCI (H/I) demonstrated that the magnitude in the reduction in recurrence risk with extended tamoxifen correlated with increasing H/I levels. In contrast, interaction P values were nonsignificant (ER, p=0.829; PR, p=0.659; AR, p=0.783; Ki67, p=0.865; AR/ER ratio, p=0.835) and the magnitude of endocrine benefit did not correlate with expression levels of any of other biomarkers. Conclusion: Results from this post-hoc analysis of the Trans-aTTom study demonstrated that whereas BCI(H/I) is a significant predictive biomarker of endocrine response, analysis of ER, PR, AR, Ki67 and AR/ER expression showed no interaction with treatment, and lacked the ability to predict benefit of extended tamoxifen in HR+ early stage breast cancer. These results add to the growing body of evidence that BCI (H/I) is distinct in its ability to predict benefit from therapy and interrogates distinct tumor biology that is not captured by other traditional biomarkers. Citation Format: Dennis C Sgroi, Kai Treuner, Yi Zhang, Tammy Piper, Ranelle Salunga, Ikhlaaq Ahmed, Lucy Doos, Sarah Thornber, Karen J Taylor, Elena F Brachtel, Sarah Pirrie, Catherine A Schnabel, Daniel W Rea, John MS Bartlett. Correlative studies of the breast cancer index (HOXB13/IL17BR) and ER, PR, AR, AR/ER ratio and Ki67 for prediction of extended endocrine benefit: A trans-aTTom study [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr GS4-09.

Purpose: The Breast Cancer Index (BCI) HOXB13/IL17BR (H/I) ratio predicts benefit from extended endocrine therapy in hormone receptor–positive (HR+) early-stage breast cancer. Here, we report the final analysis of the Trans-aTTom study examining BCI (H/I)'s predictive performance. Experimental Design: BCI results were available for 2,445 aTTom trial patients. The primary endpoint of recurrence-free interval (RFI) and secondary endpoints of disease-free interval (DFI) and disease-free survival (DFS) were examined using Cox proportional hazards regression and log-rank test. Results: Final analysis of the overall study population (N = 2,445) did not show a significant improvement in RFI with extended tamoxifen [HR, 0.90; 95% confidence interval (CI), 0.69–1.16; P = 0.401]. Both the overall study population and N0 group were underpowered due to the low event rate in the N0 group. In a pre-planned analysis of the N+ subset (N = 789), BCI (H/I)-High patients derived significant benefit from extended tamoxifen (9.7% absolute benefit: HR, 0.33; 95% CI, 0.14–0.75; P = 0.016), whereas BCI (H/I)-Low patients did not (−1.2% absolute benefit; HR, 1.11; 95% CI, 0.76–1.64; P = 0.581). A significant treatment-to-biomarker interaction was demonstrated on the basis of RFI, DFI, and DFS (P = 0.037, 0.040, and 0.025, respectively). BCI (H/I)-High patients remained predictive of benefit from extended tamoxifen in the N+/HER2− subgroup (9.4% absolute benefit: HR, 0.35; 95% CI, 0.15–0.81; P = 0.047). A three-way interaction evaluating BCI (H/I), treatment, and HER2 status was not statistically significant (P = 0.849). Conclusions: Novel findings demonstrate that BCI (H/I) significantly predicts benefit from extended tamoxifen in HR+ N+ patients with HER2− disease. Moreover, BCI (H/I) demonstrates significant treatment to biomarker interaction across survival outcomes.

Background Extending the duration of adjuvant endocrine therapy reduces the risk of recurrence in a subset of women with early-stage hormone receptor-positive (HR+) breast cancer. Validated predictive biomarkers of endocrine response could significantly improve patient selection for extended therapy. Breast cancer index (BCI) [HOXB13/IL17BR ratio (H/I)] was evaluated for its ability to predict benefit from extended endocrine therapy in patients previously randomized in the Adjuvant Tamoxifen—To Offer More? (aTTom) trial. Patients and methods Trans-aTTom is a multi-institutional, prospective–retrospective study in patients with available formalin-fixed paraffin-embedded primary tumor blocks. BCI testing and central determination of estrogen receptor (ER) and progesterone receptor (PR) status by immunohistochemistry were carried out blinded to clinical outcome. Survival endpoints were evaluated using Kaplan–Meier analysis and Cox regression with recurrence-free interval (RFI) as the primary endpoint. Interaction between extended endocrine therapy and BCI (H/I) was assessed using the likelihood ratio test. Results Of 583 HR+, N+ patients analyzed, 49% classified as BCI (H/I)-High derived a significant benefit from 10 versus 5 years of tamoxifen treatment [hazard ratio (HR): 0.35; 95% confidence interval (CI) 0.15–0.86; 10.2% absolute risk reduction based on RFI, P = 0.027]. BCI (H/I)-low patients showed no significant benefit from extended endocrine therapy (HR: 1.07; 95% CI 0.69–1.65; −0.2% absolute risk reduction; P = 0.768). Continuous BCI (H/I) levels predicted the magnitude of benefit from extended tamoxifen, whereas centralized ER and PR did not. Interaction between extended tamoxifen treatment and BCI (H/I) was statistically significant (P = 0.012), adjusting for clinicopathological factors. Conclusion BCI by high H/I expression was predictive of endocrine response and identified a subset of HR+, N+ patients with significant benefit from 10 versus 5 years of tamoxifen therapy. These data provide further validation, consistent with previous MA.17 data, establishing level 1B evidence for BCI as a predictive biomarker of benefit from extended endocrine therapy. Trial registration ISRCTN17222211; NCT00003678.

505 Background: The aTTom study is a prospective phase III trial that randomized 6953 HR+ women to stop or continue tamoxifen (TAM) for 5 more years after completing at least 4 years of prior TAM. Results at 9 years of median follow-up demonstrated fewer breast cancer recurrences (21% vs 25%; RR= 0.86 [95% CI 0.77-0.96]; P = 0.006) and reduced breast cancer mortality (13% vs 15%; HR= 0.91 [95% CI 0.80-1.04]; P = 0.18) but increased incidence of endometrial cancer with longer TAM use (P < 0.0001). The Breast Cancer Index (BCI) is a gene expression–based signature that stratifies patients based on the risk of overall (0-10y) and late (post-5y) DR and predicted the likelihood of benefit from extended endocrine therapy in MA.17. This Translational aTTom (Trans-aTTom) study is a large-scale validation of the predictive ability of BCI for extended endocrine therapy (EET) benefit. Methods: Patients treated in the aTTom trial with available primary tumor tissue were eligible for this multi-institutional prospective-retrospective study. Primary and secondary endpoints were recurrence-free interval (RFI) and disease-free interval (DFI), respectively. Statistical significance level for RFI was set at 0.0336 as per statistical plan. Kaplan-Meier and Cox proportional hazards regression analysis with time-varying coefficients were used to test the predictive activity of BCI by HoxB13/IL17BR (H/I) status (High vs Low). Likelihood ratio test based on Cox regression was used to evaluate treatment by biomarker interaction. Results: 2637 tumors were centrally assessed for ER, PR and HER2 status leading to 1822 HR+ patients analyzed (1018 N0, 583 N+). Initial results from patients with N+ disease at 12 years of median follow-up showed 287 (49%) were classified as H/I-High and 296 (51%) were classified as H/I-Low. H/I High patients showed a statistically significant benefit of 9.8% in RFI with 10y vs 5y of TAM (HR=0.35 [95% CI 0.15-0.85]; P=0.027), whereas H/I Low patients showed no benefit (-0.2% RFI; HR=1.07 [95% CI 0.69-1.65]; P=0.77). A statistically significant interaction between continuous BCI and treatment was demonstrated (P = 0.02). Conclusions: These data provide further validation and establish level 1B evidence for BCI as a predictive biomarker for preferential benefit from EET in HR+ breast cancer.

Molecular studies are part of standard care for cancer patients. Bone, a common and sometimes sole site of metastasis, requires decalcification for morphological examination. Many commonly used decalcification agents contain strong acids that degrade nucleic acids. The paradigm shift in oncology, with biomarker targeted therapy and gene expression profiling analysis, requires sufficient nucleic acid recovery from bone biopsy specimens. We systematically studied the effects of a spectrum of decalcification agents on the quantity and quality of RNA and DNA recovered from bone biopsies. Multiple bone biopsies of similar size and cellularity were fixed in 10% neutral-buffered formalin, randomized to various decalcification agents for 2 hours then processed, and embedded. Tissue lysates were obtained from unstained sections and nucleic acid isolated. DNA and RNA were quantified. Assessment of DNA and RNA integrity was accomplished by comparison of the average cycle threshold by polymerase chain reaction of selected housekeeping genes for each agent. Results were then analyzed by 2-sample t test. There was a significant decrease in both DNA and RNA yield and integrity with strong acids (hydrochloric, nitric) vs 14% EDTA and formic acid. DNA yield was (mean nanograms) 6.15 vs 68.68 (P.001) and RNA was (mean nanograms) 121.53 vs 288.89 (P=.003), respectively. DNA integrity (mean cycle threshold) was 35.79 vs 30.16 (P.001), and RNA was 33.03 vs 26.5 (P.001), respectively. Decalcification of bone biopsies with EDTA or formic acid agents was associated with a significant improvement in recovered nucleic acid quantity and quality.

Laser capture microdissection in combination with microarrays allows for the expression analysis of thousands of genes in selected cells. Here we describe single-cell gene expression profiling of CA1 neurons in the rat hippocampus using a combination of laser capture, T7 RNA amplification, and cDNA microarray analysis. Subsequent cluster analysis of the microarray data identified two different cell types: pyramidal neurons and an interneuron. Cluster analysis also revealed differences among the pyramidal neurons, indicating that even a single cell type in vivo is not a homogeneous population of cells at the gene expression level. Microarray data were confirmed by quantitative RT-PCR and in situ hybridization. We also report on the reproducibility and sensitivity of this combination of methods. Single-cell gene expression profiling offers a powerful tool to tackle the complexity of the mammalian brain.

Information on the neuroanatomical expression of a given gene is critical to understanding its function in the central nervous system. The integration of laser capture microdissection (LCM), T7-based RNA amplification and cDNA microarrays allows for this information to be simultaneously generated for thousands of genes. To validate this integrative approach, we catalogued the gene expression profiles of seven rat brain nuclei or subnuclei. A hundred cells from the following seven brain nuclei were analyzed: locus coeruleus (LC), dorsal raphe nucleus (DR), parvocellular division (PA) and magnocellular division (MG) of the hypothalamic paraventricular nucleus (PVN) and CA1, CA3 and dentate gyrus (DG) divisions of the hippocampal formation. Of the 2145 genes investigated, 1402 genes (65%) gave a hybridization signal statistically different from the background level that was defined by non-specific hybridizations to 15 different plant genes. Validation of our microarray data on four arbitrarily selected genes was confirmed by Real-Time PCR. Previous research showing expression patterns of 'signature' genes (n=17) for specific brain nuclei are consistent with our findings. For example, as previously shown, enriched mRNA expression encoding the serotonin transporter or tyrosine hydroxylase was found in DR and LC cells, respectively. Interestingly, expression of the serotonin 5-HT(2B) receptor mRNA was also found in DR cells. We confirmed this new finding by in-situ hybridization. The hierarchical clustering analysis of gene expression shows that the two divisions of the PVN (PA and MG) are closely related to each other, as well as the three regions of the hippocampal formation (CA1, CA3 and DG), which also showed similar gene expression profiles. This study demonstrates the importance, feasibility and utility of cellular brain nuclei profiling.

as well as in mammalian cell lines 6 , primary cells 7 and tissues 8 . However, present applications of microarray technology do not include the study of gene expression from individual cell types residing in a given tissue/organ (that is, in situ). Such studies would greatly facilitate our understanding of the complex interactions that exist in vivo between neighboring cell types in normal and disease states. We demonstrate here that gene expression profiles from adjacent cell types can be successfully obtained by integrating the technologies of laser capture microdissection 9 (LCM) and T7-based RNA amplification 10 with cDNA microarrays 11

The accurate determination of the risk of cancer recurrence is an important unmet need in the management of prostate cancer. Patients and physicians must weigh the benefits of currently available therapies against the potential morbidity of these treatments. Herein we describe the development of a gene expression-based continuous risk index and a validation of this test in an independent, blinded cohort of post-radical prostatectomy (RP) patients. A gene expression signature, prognostic for prostate-specific antigen (PSA) recurrence, was identified through a bioinformatic analysis of the expression of 1,536 genes in malignant prostate tissue from a training cohort of consecutive patients treated with RP. The assay was transferred to a real-time RT-PCR platform, and a continuous risk index model was constructed based on the expression of 32 genes. This 32-gene risk index model was validated in an independent, blinded cohort of 270 RP patients. In multivariate analyses, the risk index was prognostic for risk of PSA recurrence and had added value over standard prognostic markers such as Gleason score, pathologic tumor stage, surgical margin status, and presurgery PSA (hazard ratio, 4.05; 95% confidence interval, 1.50–10.94; P = 0.0057). Furthermore, RP patients could be stratified based on the risk of PSA recurrence and the development of metastatic disease. The 32-gene signature identified here is a robust prognostic marker for disease recurrence. This assay may aid in postoperative treatment selection and has the potential to impact decision making at the biopsy stage.

Background: Enumeration and molecular characterization of circulating tumor cells (CTCs) offer a non-invasive method for tumor analysis, particularly in cases where the biopsy is difficult to obtain or has been exhausted by conventional diagnostic methods. The 92-gene assay (CancerTYPE ID®, bioTheranostics, Inc.) is a clinically-validated cancer classifier based on the collective expression of 87 informative genes, many of which are involved in lineage commitment and signal transduction. The objective of this study was to determine the feasibility of a blood-based application using differential gene expression. Methods: Technical feasibility studies for cancer cell enrichment and RNA viability were conducted by purifying cancer cell lines that were spiked into donor blood using a microfluidic chip (Celsee PREP™100 system, Celsee Diagnostics) followed by immunodepletion of leukocytes. Quantitative RT-PCR and immunoflourescence were performed on cells harvested at different steps of the purification workflow. Quantitative RT-PCR on individual genes specific to each lineage compartment was used to estimate cell types and cell numbers that were co-purified by the workflow. Gene expression was evaluated by the 92-gene assay. Results: An integrated workflow was established that allows for the molecular characterization of cancer cells purified from blood. Cell enrichment on the microfluidic chip followed by purification using immunodepletion resulted in efficient recovery (> 80% of input cells) and viable RNA from cancer cells spiked into whole blood. A key result of this study was the demonstration that the level of cancer-specific gene expression in purified samples was similar to the level of expression from an equivalent number of unspiked cancer cells suggests that significant cell loss does not occur during the purification workflow. A comparison of gene expression profiles from purified and mixed cell populations allowed for the identification of subsets of genes that are differentially expressed in cancer cells compared with leukocytes. Conclusion: A functional workflow with viable RNA and efficient recovery of cancer cells that are within the technical specifications of the 92-gene assay was established. Findings from this feasibility study are fundamental to the potential for CTC-based cancer classification. Citation Format: Harris S. Soifer, Ranelle C. Salunga, Tristan G. Harris, Jose Ramirez, Priyadarshini Gogoi, Yixin Wang, Saedeh Sepehri, Kalyan Handique, Catherine A. Schnabel. Gene expression profiling for cancer classification in circulating tumor cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1391.

540Background: BCI is an 11-gene RT-PCR based assay that predicts risk of overall (0-10y) and late ( > 5y) distant recurrence, and is predictive of extended endocrine benefit in patients with early-stage, ER+ breast cancer. The objective of the current study was to evaluate the analytical performance characteristics of the BCI assay. Methods: Studies were conducted using primary resected formalin-fixed paraffin-embedded breast tumor samples. Repeatability, reproducibility, and RNA input range were assessed, and PCR linearity studies for each gene were performed. Total standard deviation (SD) was calculated as the sum of between-subject and within-subject variability estimated by linear mixed models. Assay interference studies included tumor specimens combined with patient-matched, non-tumor elements: normal tissue, activated stroma and inflammatory cells. Assay success rate due to specimen age was examined on consecutive clinical cases (N = 2667). Receiver Operating Characteristic (ROC) analysis was perfo...

Accurate determination of cancer origin is necessary to guide optimal treatment but remains a diagnostic challenge. Gene expression profiling technologies have aided the classification of tumors and, therefore, could be applied in conjunction with clinicopathologic correlates to improve accuracy. We report an expanded version of the previously described 92-gene assay to classify 30 main tumor types and 54 histological subtypes, with coverage of ≥95% of all solid tumors based on incidence. Increased tissue coverage was achieved through expansion of a reference tumor database containing 2,206 specimens, with a median of 62 samples per main tumor type. The 92-gene classification algorithm demonstrated sensitivities of 87% and 85% for 30 main types and 54 histological subtypes, respectively, in leave-one-out cross validation, and 83% in a test set of 187 tumors representing 28 of the 30 main cancer types. These findings provide further support that broad and diverse tumor classification can be performed using a relatively compact gene set. An additional 300 consecutive cases submitted for clinical testing were profiled to characterize clinical utility in a real-world setting: the 92-gene assay confirmed 78% of samples having a single suspected primary tumor and provided a single molecular prediction in 74% of cases with two or more differential diagnoses. Further development of the 92-gene RT-PCR assay has resulted in a significant expansion in reportable tumor types and histological features with strong performance characteristics and supports the use of molecular classification as an objective standardized adjunct to current methods.

Background: A dichotomous index combining two gene expression assays, HOXB13:IL17BR (H:I) and molecular grade index (MGI), was developed to assess risk of recurrence in breast cancer patients. The study objective was to demonstrate the prognostic utility of the combined index in early-stage breast cancer. Methods: In a blinded retrospective analysis of 588 ER-positive tamoxifen-treated and untreated breast cancer patients from the randomized prospective Stockholm trial, H:I and MGI were measured using real-time RT-PCR. Association with patient outcome was evaluated by Kaplan-Meier analysis and Cox proportional hazard regression. A continuous risk index was developed using Cox modeling. Results: The dichotomous H:I+MGI was significantly associated with distant recurrence and breast cancer death. The >50% of tamoxifen-treated patients categorized as low-risk had

Purpose: Histologic tumor grade is a well-established prognostic factor for breast cancer, and tumor grade–associated genes are the common denominator of many prognostic gene signatures. The objectives of this study are as follows: (a) to develop a simple gene expression index for tumor grade (molecular grade index or MGI), and (b) to determine whether MGI and our previously described HOXB13:IL17BR index together provide improved prognostic information. Experimental Design: From our previously published list of genes whose expression correlates with both tumor grade and tumor stage progression, we selected five cell cycle–related genes to build MGI and evaluated MGI in two publicly available microarray data sets totaling 410 patients. Using two additional cohorts (n = 323), we developed a real-time reverse transcription PCR assay for MGI, validated its prognostic utility, and examined its interaction with HOXB13:IL17BR. Results: MGI performed consistently as a strong prognostic factor and was comparable with a more complex 97-gene genomic grade index in multiple data sets. In patients treated with endocrine therapy, MGI and HOXB13:IL17BR modified each other's prognostic performance. High MGI was associated with significantly worse outcome only in combination with high HOXB13:IL17BR, and likewise, high HOXB13:IL17BR was significantly associated with poor outcome only in combination with high MGI. Conclusions: We developed and validated a five-gene reverse transcription PCR assay for MGI suitable for analyzing routine formalin-fixed paraffin-embedded clinical samples. The combination of MGI and HOXB13:IL17BR outperforms either alone and identifies a subgroup (∼30%) of early stage estrogen receptor–positive breast cancer patients with very poor outcome despite endocrine therapy.

Although distinct pathological stages of breast cancer have been described, the molecular differences among these stages are largely unknown. Here, through the combined use of laser capture microdissection and DNA microarrays, we have generated in situ gene expression profiles of the premalignant, preinvasive, and invasive stages of human breast cancer. Our data reveal extensive similarities at the transcriptome level among the distinct stages of progression and suggest that gene expression alterations conferring the potential for invasive growth are already present in the preinvasive stages. In contrast to tumor stage, different tumor grades are associated with distinct gene expression signatures. Furthermore, a subset of genes associated with high tumor grade is quantitatively correlated with the transition from preinvasive to invasive growth.

10582 Background: Gene expression profiles may have higher predictive power for relapse over traditional clinicopathologic prognostic factors. TBCI combines the 2-gene index HOXB13:IL17BR and a 5-g...

Background. HOXB13:IL17BR (H:I) is a two gene expression index, which has been shown to be an independent prognostic factor in estrogen receptor (ER)-positive lymph node-negative (N0) breast cancer. A molecular grade index (MGI) measures the expression of five proliferation-related genes. An algorithm based on dichotomized H:I and MGI stratifying patients into three risk groups has been shown to be superior to either alone in predicting risk of distant metastasis in ER+/N0 patients. Further validation in larger cohorts is needed to establish its clinical performance. A continuous predictor combining H:I and MGI is desirable for making individualized risk assessment in the clinical setting.Methods. During 1976 through 1990 the Stockholm Breast Cancer Group conducted a randomized clinical trial comparing adjuvant tamoxifen with control in 1780 postmenopausal women considered to be at low risk of recurrence (N0 and tumor size < 3 cm). We measured H:I and MGI using a real time PCR assay in 769 patients from this trial based on sample availability. Correlation of gene expression indices with distant metastasis and death due to breast cancer was evaluated by Kaplan-Meier analysis and Cox proportional hazard regression. Modeling was also used to develop a continuous risk index as a function of both H:I and MGI.Results. Using pre-specified cutoff points and combination algorithm, H:I, MGI and their combination each was significantly associated with both distant metastasis-free survival and breast cancer-specific survival (Table 1). Furthermore, we used the ER+ tamoxifen-treated subset (n=314) to develop a continuous risk model (Breast Cancer Index or BCI) combining both H:I and MGI. The prognostic utility of BCI was then successfully validated in the untreated subset in this trial and three additional previously published cohorts. BCI consistently identified ∼50% patients with a very low 10-year recurrence risk (< 5%).Discussion. This large retrospective analysis of a randomized clinical trial cohort validated the prognostic utility of H:I, MGI, and their combination. With the continuous risk model, this RT-PCR-based assay allows prediction of risk of recurrence at the individual level, which may help tailor personalized treatment strategy.Table 1. Univariate Cox regression analysis of H:I, MGI, and H:I+MGI Distant Metastasis Breast Cancer Death Tam (n=398)No Tam (n=371)Tam (n=398)No Tam (n=371) Hazard Ratio (95%)Hazard Ratio (95%)Hazard Ratio (95%)Hazard Ratio (95%)H:Ihigh vs low1.93 (1.07-3.47) _ p=2.80E-022.50 (1.54-4.07) p=2.26E-042.40 (1.28-4.52) p=6.49E-032.23 (1.35-3.69) p=1.74E-03MGIhigh vs low4.62 (2.29-9.32) _ p=1.86E-051.93 (1.19-3.12) p=7.71E-035.25 (2.43-11.34) p=2.48E-052.67 (1.56-4.58) p=3.50E-04H:I+MGIinter. vs low3.86 (1.71-8.69) _ p=1.24E-031.32 (0.69-2.50) p=4.00E-013.99 (1.63-2.82) p=2.46E-032.08 (1.07-4.03) p=3.06E-02 high vs low5.29 (2.51-11.18) p=1.26E-052.42 (1.44-4.06) p=8.00E-046.34 (2.82-14.24) p=7.77E-063.14 (1.77-5.57) p=9.67E-05 Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 77.

22061 Background: We have previously reported two complementary prognostic markers in breast cancer for predicting risk of recurrence: a molecular grade index (MGI) that recapitulates tumor grade a...

Tamoxifen significantly reduces tumor recurrence in certain patients with early-stage estrogen receptor-positive breast cancer, but markers predictive of treatment failure have not been identified. Here, we generated gene expression profiles of hormone receptor-positive primary breast cancers in a set of 60 patients treated with adjuvant tamoxifen monotherapy. An expression signature predictive of disease-free survival was reduced to a two-gene ratio, HOXB13 versus IL17BR, which outperformed existing biomarkers. Ectopic expression of HOXB13 in MCF10A breast epithelial cells enhances motility and invasion in vitro, and its expression is increased in both preinvasive and invasive primary breast cancer. The HOXB13:IL17BR expression ratio may be useful for identifying patients appropriate for alternative therapeutic regimens in early-stage breast cancer.