Abstract PD3-18: The NSABP/NRG 8-gene signature accurately predicts degree of benefit from trastuzumab in Alliance/NCCTG N9831: Validation of the 8-gene signature in an independent clinical trial

BACKGROUND

Clinical trials NRG/NSABP B-31 and Alliance/NCCTG N9831 demonstrated that trastuzumab added to chemotherapy extended disease-free survival (DFS) in breast cancer patients (pts) in the adjuvant setting (Romond et al). However, the degree of trastuzumab benefit varies among pts. We previously described an 8-gene model that was predictive for trastuzumab benefit, which was validated in an independent cohort of B-31 patients (Pogue-Geile 2013). The 8-gene signature subtyped B-31 pts in an independent validation cohort into three trastuzumab benefit groups: one with large benefit HR=0.27, one with medium benefit HR=0.56, and one with little to no benefit HR=1.56. The purpose of this study was to validate the 8-gene model in pts enrolled into N9831, which tested the efficacy of adding trastuzumab to doxorubicin plus cyclophosphamide → paclitaxel by randomizing pts into one of three arms: chemotherapy only (Arm A), trastuzumab given after chemotherapy (Arm B), or trastuzumab given concurrently with chemotherapy (Arm C).

METHODS

NCounter data, consisting of 8 predictive genes and 4 house-keeping genes, for 1,379 patients enrolled into N9831 were used to assign each patient to one of the three benefit groups. Assignments to one of the three trastuzumab benefit groups based on the 8-gene model were made by the NSABP/NRG Pathology Laboratory Biostatistician, who was blinded to outcome data. Predictions were sent to N9831 investigators and accuracy of predictions were tested in pts enrolled into arms A and C (N=892) using Cox models adjusted for age, nodes, ER/PR status, tumor size, and grade. Recurrence-free survival (RFS) and DFS were used as endpoints.

RESULTS

Tumors from N9831were placed into one of the three trastuzumab benefit groups based on the 8-gene signature. The N9831 pts in the predicted-large benefit group had a hazard ratio (HR) of 0.47, P=0.0006, pts in the predicted-medium benefit group had an HR of 0.6, P=0.02, and the predicted-low benefit group had an HR of 1.54, P=0.375. The interaction P value was significant at 0.019 in adjusted Cox models. The RFS at 10 years for trastuzumab-treated pts was 72%, 83%, and 83% in the low, medium, and large benefit groups, respectively.

CONCLUSIONS

The 8-gene signature developed in a discovery cohort and validated in an independent cohort of B-31 pts has now been validated in N9831. Many anti-HER2 therapies such as lapatinib, afatinib, neratinib, pertuzumab, and TDM-1 have been approved for treatment in metastatic breast cancer pts and although these agents have shown responses, the actual improvements in outcomes in pts is variable and it is not known which pts actually receive benefit. Thus this signature may be clinically useful in identifying pts who may benefit from additional treatment beyond trastuzumab or for stratification of pts enrolled into clinical trials testing new anti-HER2 therapies.

SUPPORT

U10CA180868, -180822, UG1CA189867, U24CA196067, the PA DOH, which disclaims certain responsibilities, Genentech, Inc., and the Breast Cancer Research Foundation.

Citation Format: Pogue-Geile KL, Song N, Serie DJ, Thompson EA. The NSABP/NRG 8-gene signature accurately predicts degree of benefit from trastuzumab in Alliance/NCCTG N9831: Validation of the 8-gene signature in an independent clinical trial [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD3-18.

Differential gene expression profiling of matched primary renal cell carcinoma and metastases reveals upregulation of extracellular matrix genes

Background

The majority of renal cell carcinoma (RCC) studies analyze primary tumors, and the corresponding results are extrapolated to metastatic RCC tumors. However, it is unknown if gene expression profiles from primary RCC tumors differs from patient-matched metastatic tumors. Thus, we sought to identify differentially expressed genes between patient-matched primary and metastatic RCC tumors in order to understand the molecular mechanisms underlying the development of RCC metastases.

Patients and methods

We compared gene expression profiles between patient-matched primary and metastatic RCC tumors using a two-stage design. First, we used Affymetrix microarrays on 15 pairs of primary RCC [14 clear cell RCC (ccRCC), 1 papillary] tumors and patient-matched pulmonary metastases. Second, we used a custom NanoString panel to validate seven candidate genes in an independent cohort of 114 ccRCC patients. Differential gene expression was evaluated using a mixed effect linear model; a random effect denoting patient was included to account for the paired data. Third, The Cancer Genome Atlas (TCGA) data were used to evaluate associations with metastasis-free and overall survival in primary ccRCC tumors.

Results

We identified and validated up regulation of seven genes functionally involved in the formation of the extracellular matrix (ECM): DCN, SLIT2, LUM, LAMA2, ADAMTS12, CEACAM6 and LMO3. In primary ccRCC, CEACAM6 and LUM were significantly associated with metastasis-free and overall survival (P < 0.01).

Conclusions

We evaluated gene expression profiles using the largest set to date, to our knowledge, of patient-matched primary and metastatic ccRCC tumors and identified up regulation of ECM genes in metastases. Our study implicates up regulation of ECM genes as a critical molecular event leading to visceral, bone and soft tissue metastases in ccRCC.

Abstract PD5-06: Prognostic value of molecular tumor infiltrating lymphocyte (mTIL) signatures in HER2-positive breast cancer patients in N9831 and FinHer/FinXX trials

Background: While previous study showed that the enrichment of immune-related gene expression was associated with outcome in HER2+ patients receiving sequential or concurrent trastuzumab (H), stromal tumor infiltrating lymphocytes (sTIL) have not been consistently shown to associate with outcome in this group of patients. Given that TIL scoring may be subjective, we analyzed molecular signatures of different subsets of tumor infiltrating immune cell populations, using NanoStringTM gene expression data to assess molecular TIL (mTIL) signature enrichment and intrinsic subtype as a function of relapse-free survival (RFS).

Methods: NanoStringTM technology was used to quantify mRNA in samples from 1,280 patients in N9831, 168 patients in FinHer, and 170 patients in FinXX. In N9831, patients in arm A were treated with chemotherapy alone (AC-T), arm B received chemotherapy followed by sequential H (AC-T-H), and arm C received H concurrently with chemotherapy (AC-TH). In the FinHer trial, H was given concurrently for 9 weeks and either 1 year or 9 weeks in FinXX trial. Cox proportional hazard ratio (HR) was used to determine the association of each gene signature with RFS. Different immune subset signatures, including CD45, B-cells, CD8 T-cells, cytotoxic-cells, and T-cells were analyzed using algorithms developed by NanoString.

Results: In N9831, CD45, cytotoxic-cell, and T-cell signatures were significantly associated with improved RFS in patients receiving chemotherapy alone and AC-T-H. However, none of the mTIL signatures were significantly associated with outcome in patients receiving AC-TH. Patients lacking CD45 enrichment had better outcome when H was given concurrently with chemotherapy. The 10-year Kaplan-Meier estimates for RFS in arm B patients with CD45 enrichment or no enrichment were 81.3% and 72.6%, respectively (HR 0.63 [95% CI, 0.42-0.93]; p = 0.02), and in arm C were 83.6% and 79.8%, respectively (HR 0.79, 95%CI 0.49-1.28; p = 0.34). Among patients with HER2-enriched subtype, all of the mTIL signatures were associated with improved RFS in arm A (AC-T) and B (AC-T-H) but remained non-significant in arm C (AC-TH). In patients with luminal subtypes, mTIL signatures were not significantly associated with outcome in patients treated with chemotherapy alone. Similar findings were observed in the FinHer and FinXX trials, in which, none of mTIL signatures were significantly associated with outcome among patients who received H.

Conclusion: This analysis sheds light on previous discrepancy between immune-related gene signature and sTIL findings. Our data also suggests that the poor prognosis associated with lack of infiltrating immune cells can be partly overcome by the concomitant administration of H with chemotherapy. mTIL signatures, specifically CD45, cytoxic, and T cells, were prognostically associated with improved outcome in patients receiving chemotherapy without concurrent trastuzumab. Understanding the role of the immune system in response to H will require a higher degree of granularity than can be achieved by histological quantification of TILs. Further studies are needed to validate the significance of mTIL signatures as predictive or prognostic biomarker in HER+ patients.

Citation Format: Chumsri S, Serie DJ, Mashadi-Hossein A, Tenner KS, Lauttia SL, Moreno-Aspitia A, McLaughlin SA, Nassar A, Warren S, Danaher P, Colon-Otero G, Lindman H, Joensuu H, Perez EA, Thompson EA. Prognostic value of molecular tumor infiltrating lymphocyte (mTIL) signatures in HER2-positive breast cancer patients in N9831 and FinHer/FinXX trials [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr PD5-06.

Abstract P2-09-03: Mammographic Density Response to Aromatase Inhibitor Therapy

Background: Mammographic density, the variation in fat, epithelial and stromal tissues seen on screening mammography, is a strong risk factor for breast cancer and can be modified by hormonal agents. Changes in density from tamoxifen or postmenopausal hormone (PMH) use are associated with risk, suggesting that density may be a surrogate marker of therapeutic efficacy. Aromatase inhibitors (AIs) are given as adjuvant therapy in hormone receptor positive postmenopausal breast cancer and are known to decrease levels of estrone and estradiol in both serum and breast tissue. Our goal here was to examine the influence of AIs on mammographic density in women with early breast cancer.

Methods: We conducted a case-control study of postmenopausal breast cancer patients initiating adjuvant AI therapy (anastrozole or exemestane) on protocols NCIC CTG MA27, NCCTG N063I and MC (Mayo Clinic) 0532. Eligibility included; an intact contralateral breast with no prior surgery; a screening mammogram within twelve months before AI initiation and at 9-15 months on therapy; no prior endocrine therapy and informed consent. Controls were sampled from the Mayo Mammography Health Study, a cohort of 19,924 receiving screening mammography at the Mayo Clinic, and matched to cases on age, prior PMH use, baseline body mass index (BMI) and interval between mammograms. Pre-treatment and on-study mammograms for cases (corresponding mammograms for controls) were digitized. Change in percent density was estimated on the craniocaudal view of the non-cancerous breast using two methods: a subjective assessment of change by an expert radiologist (within 5%; 5-10% increase, 10-25% increase, 25%+ increase, 5-10% decrease, 10-25% decrease and 25%+ decrease) and a quantitative assessment of absolute change using a computer-assisted thresholding program (Cumulus). Analyses compared magnitude of change in density by both the subjective and quantitative methods between cases and matched controls. Results: 574 pairs were eligible for analyses (MA27-505 cases; N063I-12 cases; MC0532-57 cases). Characteristics of the two groups are shown in the table below. Using either density estimation method, there was a greater decrease in density among women on AI therapy vs. matched controls. In 33% (95% CI: 29-37%) of pairs, there was at least a one greater category decrease for the case relative to her control by subjective estimation. In 14% (95% CI: 11-18%) of the pairs, there was at least a 5% greater decrease for the case relative to her control by quantitative estimation. Data will be available according to AI class (non-steroidal versus steroidal) in November.

Conclusions: In the largest report to date to examine the influence of AI therapy on mammographic density, we provide evidence that AI is associated with decreases in density in a small subgroup of women. We are currently examining factors that influence these AI-associated decreases in density and whether these differences are unique to one class of AI. (Supported in part by NIH grants P50CA116201, U01GM61388, U10CA77202, U10CA25224)