Abstract P3-06-11: Time-course DNA and RNA profiling of tumors from intra-patient cross-over trial of sequential use of aromatase inhibitors

Background. The NEO-LET-EXE trial examines the neoadjuvant use of sequential administration of the aromatase inhibitor letrozole (Femar / Femara) and the aromatase inactivator exemestane (Aromasin). Although both drugs nearly completely inhibit aromatase, resistance to both is developed with time. However, when used sequentially, in some patients after switching to the alternative drug and progressing on the first choice, new responses may appear. The mechanism behind this clinical observation is currently not known. The solution may lead to a novel strategy to re-sensitize tumors to hormonal treatment. Prior studies have examined genomics at the four month time point, but not at both two months and four months.

Material. Postmenopausal patients with estrogen receptor (ER) positive (>50%), HER-2 negative locally advanced breast cancer may be enrolled. Age: 18+ (no upper limit). Present accrural and target accrural: 49 out of planned 100 patients have been enrolled so far. The last patient is expected to enter the trial in Q4 2019.

Study design. In the neoadjuvant, randomized, open-label, intra-patient cross-over trial NEO-LET-EXE biopsies are taken before treatment, after two months on one aromatase inhibitor and swap to the other aromatase inhibitor, and at surgery at four months.

Results. In order to explain the phenomenon of a lack of cross-resistance between steroidal and non-steroidal aromatase inhibitors we profiled biopsies at three time points per patient by whole exome and whole transcriptome sequencing from FFPE from 25 patients. A total of 56 DNA whole exomes and 41 RNA seq transcriptomes were generated from FFPE samples available. When grouping both arms together, mutational burden decreased at two months, while clonality of mutations increased, providing evidence of selection. At four months, mutational burden increased from the two month timepoint. In particular, PIK3CA somatic variants present at the first time point were not detected at two months. However, these were detected again at significant variant allele fractions at four months after switch of treatment. The majority of gene expression changes happen in the initial two months, with fewer changes between two and four months. Instead, significant changes in alternative splicing at two months and four months were observed, for example for FGFR1, which does not experience a large fold change in expression between these two points. Our preliminary results show significant DNA and RNA changes in the first two months of aromatase inhibition leading to fewer, more clonal variants. Comparison of the four month to two month time point shows fewer RNA changes than the prior two months and an increase in the number of somatic variants compared to the two month timepoint.

Citation Format: Vaske CJ, Parulkar R, Bahrami N, Sauer T, Loeng M, Gravdehaug B, Aljabri B, Bemanian V, Lindstrøm J, Lüders T, Kristensen V, Geisler J. Time-course DNA and RNA profiling of tumors from intra-patient cross-over trial of sequential use of aromatase inhibitors [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 P3-06-11.

Abstract P2-04-26: Identifying patient-specific neoepitopes for cell-based and vaccine immunotherapy across breast cancer classifications reveals rarely shared recurrent neoepitopes

Introduction: Targeted therapies for breast cancers such as trastuzumab and everolimus have durable clinical benefits for patients that express the relevant biomarkers (HER2 and mTOR respectively). Triple negative breast cancer patients lack these biomarkers and are left with few options. Recent advances in immunotherapy agents against PD-1/CTLA4 for patients with melanoma have yielded amazing clinical benefits for a subset of patients and may have similar results in breast cancer patients, but again the vast majority of patients still undergo disease progression. We analyzed whole genome sequencing (WGS) and RNA sequencing data from The Cancer Genome Atlas (TCGA) to identify neoepitopes among breast cancer patients that could be used to develop next-generation, patient-specific cancer immunotherapies. Neoepitopes are tumor specific markers that arise from mutations acquired from cancer and may represent a path to targeted therapies even in triple negative breast cancers.

Results: We analyzed 99 breast cancer patients from TCGA, containing a mixture of PR+/HER2+/ER+ and TNBC classifications. These breast cancer patient samples were selected by the availability of whole genome sequencing (WGS) data, RNA-sequencing data as well as clinical outcome data. We identified an average of 680 potential neoepitopes per patient based solely on WGS data. To further refine and select high quality neoepitopes we restricted these neoepitopes based on gene expression yielding an average of 304 expressed neoepitopes per patient. We predicted each patient's HLA typing using only omics data, which we then used to predict HLA-expressed neoepitope binding analysis resulting in an average of 11 high-quality tumor specific neoepitopes per patient. We identified few recurrent neoepitopes that were bound and expressed, indicating the need for a personalized medicine approach.

Conclusions: Within the TCGA dataset, the majority of neoepitopes among patients with breast cancer were unique to each patient. Rarely within subsets of breast cancers such as HER2+, we identify neoepitopes that are shared between patients. For breast cancer patients who do not respond to targeted therapies, high-throughput identification of neoepitopes could serve as the basis for the development of next-generation, patient-specific immunotherapies.

Citation Format: Nguyen A, Sanborn JZ, Vaske CJ, Rabizadeh S, Niazi K, Soon-Shiong P, Benz SC. Identifying patient-specific neoepitopes for cell-based and vaccine immunotherapy across breast cancer classifications reveals rarely shared recurrent neoepitopes [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 P2-04-26.

Abstract P6-04-14: Integrating whole genome sequencing data with RNAseq, pathway analysis, and quantitative proteomics to determine prognosis after standard adjuvant treatment with trastuzumab and chemotherapy in primary breast cancer patients

Background: Despite improvements in the treatment of HER2+ breast cancer (BC), almost all patients (pts) progress in the metastatic setting. Three examples of resistance mechanisms are: PI3K mutations, lack of ADCC, or low expression of HER2. We recently showed that among 237 pts who had HER2 amplifications, 49% had normal or low levels of HER2 RNA. In addition, quantification of HER2 protein by selected reaction monitoring mass spectrometry (SRM-MS) accurately predicted HER2 expression status compared with IHC (3+)/ISH (≥2.0). Here we report a comprehensive panomic approach that integrates whole genome sequencing (WGS), RNASeq, quantitative proteomics, and pathway analysis to determine associations between tumor molecular profiles and prognosis among HER2+ pts.

Methods: Matched tumor-normal samples (FFPE tumors and blood) were obtained from 58 pts with HER2+ BC who had received standard adjuvant chemotherapy and trastuzumab. Pts were divided into 2 groups: those who had no recurrence after 5 years and those who had developed metastases. The HER2 status of each pt was previously determined using IHC/FISH. Samples underwent WGS and RNASeq according to NantOmics CLIA-approved assay specifications. WGS data were processed using Contraster; RNASeq data confirmed the presence of gene mutations and was used to identify mutational and transcript abundance. PARADIGM was used to reveal associations between gene mutations and pathway levels. SRM-MS was used for proteomics analysis of a panel of 53 proteins. Tumor areas from FFPE tissue sections were analyzed after laser microdissection. Absolute protein quantitation was accomplished through simultaneous detection of endogenous target and synthetic labeled heavy peptide identical to analytical targets. Genetic alterations in germline and tumor DNA were compared in pts with vs without recurrence.

Results: There was no statistically significant difference in the mean concentration of HER2 in the tumors of pts with vs without recurrence: 2.34 fmol/µL vs 2.56 fmol/µL. Other analyzed proteins did not appear to be associated with recurrence; however, expected correlations between pt and tumor characteristics and protein expression were found. With regard to clinically relevant mutations, we found one germline BRCA2 mutation in a pt with no family history of this mutation. The most commonly found somatic mutations were in TP53 (11 pts), AMBRA1 (11 pts), MORC4 (10 pts), SETD2 (8 pts), CDC27 (6 pts), BCLAF1 (5 pts), ZNF479 (4 pts) , PIK3CA (3 pts), PIK3R1 (3 pts), RUNX1 (3 pts), and GATA3 (3 pts).

Conclusion: Whereas HER2 expression status was predictive of OS and PFS in pts treated with trastuzumab (Nuciforo et al. Mol Onc. 2015), in this small exploratory study of HER2+ BC pts, HER2 expression status was not predictive of recurrence. To better understand the molecular mechanisms driving recurrence beyond HER2 status alone, genomic sequencing may define a signature of recurrence after anti-HER2 therapy.

Citation Format: Benz SC, Rabizadeh S, Cecchi F, Beckman MW, Brucker SY, Hartmann A, Golovato J, Hembrough T, Janni W, Rack B, Sanborn JZ, Schneeweiss A, Vaske CJ, Soon-Shiong P, Fasching PA. Integrating whole genome sequencing data with RNAseq, pathway analysis, and quantitative proteomics to determine prognosis after standard adjuvant treatment with trastuzumab and chemotherapy in primary breast cancer patients. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-04-14.

Abstract P4-14-02: Molecular response in breast cancer tumors treated with neoadjuvant chemotherapy with and without bevacizumab: Results from NeoAva - A randomized phase II study

Patients treated with bevacizumab in addition to regular neoadjuvant chemotherapy achieve an increased rate of pathological complete response (pCR). The molecular characteristics of responding and non-responding tumors, including how treatment combinations influence the gene expression profiles and the signaling pathways, may be useful predictors of antiangiogenic response.

The NeoAva study included patients with HER2 negative primary tumors of ≥25 mm that were randomized (1:1) to receive neoadjuvant chemotherapy (4 x FEC100 + 12 weeks of taxane-based therapy) with or without bevacizumab. Mammography, ultrasound and MR imaging were used for response evaluation, in addition to final pathology assessment.

In the first part of the study 74 patients were evaluable for tumor response. The tumor size at time of inclusion was T2, T3 and T4 in 24.3%, 67.6% and 8.1% of the patients, respectively. Lymph node metastases were detected in 56.7% of the patients at inclusion and 82.4% were hormone receptor positive. There were no significant differences in the tumor size, lymph node or hormone receptor status between the treatment arms.

The patients were randomized with bevacizumab + chemotherapy (n = 37) and treatment with chemotherapy alone (n = 37). Of the nine patients who achieved pCR in breast and axilla (12.2%), seven patients received bevacizumab (7/37), while two were treated with chemotherapy alone (2/37). Four of the patients with pCR were hormone receptor negative, of which three received bevacizumab. Of the remaining five hormone receptor positive tumors that achieved complete response, four received bevacizumab.

In the second part of the study we evaluated gene expression signatures by RNA microarray and the time-response of pathways to treatment, using pathway analysis that integrates copy number and gene expression (Paradigm). Biopsies for molecular analyses were collected before therapy, after 12 weeks, and at surgery. Treatment associated gene expression changes to chemotherapy were subtracted, and bevacizumab associated differential expression was observed for 1069 genes. Furthermore, molecular profiling of the tumor tissue was performed at DNA level by copy number analysis (Affymetrix, SNP6.0) and mRNA level by gene expression arrays(Agilent 60K). At the screening time point, we found high proliferation through the activity of cyclin E and B and the transcription factors E2F1 and FOXM1. At 12 weeks, there was a strong increase in predicted p53 signaling, due to increased activity of downstream target genes. The 12 week timepoint was also characterized by an increase of Calmodulin 1, MAPK3, as well as Peroxisome proliferator-activated receptor alpha (PPAR-alpha), and both trends continued to the 25 week time point. At 25 weeks, there were broad increases in ERK1/2, JUN, and FOS signaling. The 25 week timepoint also showed a T-cell response signature that from increased activity of GATA3, IL6/IL6R, IL4, and NFATC1 and NFATC2. These results suggest that there are measurable and strongly significant aberrations in molecular activity during treatment, which may be useful to monitor treatment response.

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-14-02.

Abstract P2-06-05: Single-cell RNA sequencing of paclitaxol-treated breast cancer cell lines to find individual cell response

Cancer treatments act on a population of cells, each of which may experience different individual responses to treatment. Such differential response will result in resistance to treatment even if a majority of cancerous cells are eliminated. To examine differential cell response, we simultaneously profiled the gene expression and mutation spectrum of individual cells from the MDAMB231 cell line using next generation sequencing of isolated RNA. A total of 23 transcriptomes were characterized from paclitaxel-treated and paclitaxel-surviving cells. We found significant different changes in mutation rates between paclitaxel treated cells, with a dose-dependent increase in single nucleotide changes in RNA in paclitaxel-treated cells. Cells undergoing exposure to paclitaxel also showed higher pathway activity in SRC, as well as an integrin switch from ITGB1 to ITGB3. In contrast, cells that survived a high dose of paclitaxel showed an insignificant number of single nucleotide changes, suggesting that these cells either evaded initial paclitaxel exposure or were better able to repair the effects of paclitaxel exposure. Despite the RNA sequence similarity between surviving and untreated cells, there were changes in gene expression and pathway activities including higher PI3K activity. Paclitaxel-surviving cells also showed activation of pathways associated with higher proliferation.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-06-05.

P1-06-09: Patient-Specific Integrative Pathway Analysis Using PARADIGM Identifies Key Activities in I-SPY 1 Breast Cancer Patients (CALGB 150007/150012; ACRIN 6657)

Background: A major challenge in interpreting high-throughput multianalyte genomic data sets such as those produced by the ISPY clinical trials is data integration and interpretation within the context of biologically relevant pathways. To address this need, the data analysis tool PARADIGM (PAthway Recognition Algorithm using Data Integration on Genomic Models) was developed to infer the activities of genetic pathways by integrating any number of functional genomic data sets for a given patient sample into a pathway activity profile.

Methods: We used PARADIGM to integrate gene expression (Agilent 44K) and DNA copy number data (AFFY 22K and 330K MIP) from 133 ISPY-1 patients into pathway component activity levels for approximately 1400 curated signal transduction, transcriptional and metabolic pathways superimposed onto a single non-redundant ‘SuperPathway'. These pathway activities then become the substrate for statistical analyses to identify pathways characterizing different breast cancer subtypes, as well as those associated with recurrence and response to neoadjuvant chemotherapy within breast cancer subgroups. To identify subtype-specific pathway activities, we used ANOVA for initial feature filtering followed by Tukey analysis with Benjamini Hochberg multiple testing correction. For other binary outcome comparisons we used Mann-Whitney (2-sample Wilcoxon) analysis. PARADIGM results were corroborated with pathway enrichment analysis and filtered for significance.

Results: In agreement with breast cancer cell line and other prior studies, basal-like and triple negative cancers are dominated by upregulation of the FOXM1 and MYC/Max subnetworks and downregulation of the FOXA1/ER signal transduction pathway, the converse of the activity pattern seen in luminal breast cancers. These and other subtype associations pass stringent multiple testing corrected significance tests. Though an association study of recurrence over the entire patient cohort mostly yields pathways characteristic of basal-like tumors, alternative pathway associations emerge when subtypes are analyzed individually for outcome and significance tests are relaxed to include features that pass un-corrected Wilcoxon significance tests and also generate highly significant pathway enrichment scores. Subtype-specific drivers of recurrence and chemo-resistance supported by this level of evidence include ALK1/2 (TGFB-BMP) and p53 effector signaling for basals and Syndecan-1 and c-MYC for luminals. Chemo-sensitivity pathways, assessed by association with pCR and RCB1, appear to be subtype-specific as well, with HDAC class 1 signaling, LRP6-Wnt, and IRE1alpha chaperones dominating basal-like cancers and c-MYB activity dominating Her2+ cancers, whereas chemo-sensitivity of HR+Her2- cancers though rare appears to be driven by the DNA damage axis (BRCA/BARD1). Conclusion: These and other similar analyses suggest that patients with TN or basal-like disease might benefit from the addition of ALK1 pathway inhibitors to treatment, whereas high risk HR+ patients might benefit from Syndecan-1 inhibitors. C-MYC/MAX inhibitors might benefit all high risk patients.

Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-06-09.

P3-06-07: Integrated Genomic and Pathway Analysis Reveals Key Pathways across Breast Subtypes

Cancer is a disease of genomic perturbations that lead to dysregulation of multiple pathways within the cellular system. While common pathways are believed to be shared within specific cancer types, the mechanisms behind why particular patients respond differently to treatment is not well understood. Genomics studies such as The Cancer Genome Atlas (TCGA) and Stand Up To Cancer (SU2C) attempt to address this issue by collecting large-scale whole-genome measurements of mRNA expression, DNA copy number, and epigenetic features. Common analysis of these measurements integrates data across multiple samples to distinguish signal from noise. However, serious challenges remain in identifying genomic features and pathways significant for prognosis and clinical treatment classifications.

We have created the Five3 Analysis Pipeline to streamline discovery of individual samples’ mutations, small indels, copy number alterations, genome rearrangements, expression changes, and resulting pathway activities. This pipeline is capable of processing and integrating data from both next generation sequencing and microarray platforms in the analysis of single or multiple tumor samples. Our sequence analysis corrects for both tumor sample impurity and germline variation to accurately identify somatic mutations present in the tumor. Our pathway analysis incorporates gene copy number, mutations, expression, and promoter methylation on a superimposed pathway constructed from several curated pathway databases in a sample-specific manner.

By applying this pipeline to the TCGA breast cancer datasets, we recapitulate established breast subtypes at a pathway-dependent level. For example, basal tumors appear enriched for proliferation pathways compared to luminal samples within this cohort. Expanding the pathway analysis to include TCGA lung cancer samples, we find similar subnetworks activated between basal and squamous lung and between luminal and lung adenocarcinomas. This hints at similar genomic mechanisms for these subtypes independent of tissue of origin. Finally, by analyzing genomic alterations across all breast cancers we see mutational clusters in PIK3CA that correspond with publicly-available hotspots [1]. As suggested by previous reports [2], we find that samples with mutations clustered in exon 10 exhibit differential pathway activities relative to those samples with mutations clustered in exon 21, independent of subtype and TP53 mutation status. These results show the power of this integrated genomic platform in elucidating pathway signatures and the need to consider cross cancer analyses to identify shared tumorigenic mechanisms that may suggest common therapeutic targets.

[1] Forbes, S.A et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucl. Acids Res. (2011) 39: D945-D950

[2] Vasudevan KM et al. AKT-independent signaling downstream of oncogenic PIK3CA mutations in human cancer. Cancer Cell 2009 Jul.;16(1):21–32.

Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P3-06-07.

Inferring disease-related pathways using a probabilistic epistasis model

We present a probabilistic model called a Joint Intervention Network (JIN) for inferring interactions among a chosen set of regulator genes. The input to the method are expression changes of downstream indicator genes observed under the knock-out of the regulators. JIN can use any number of perturbation combinations for model inference (e.g. single, double, and triple knock-outs). RESUITS/CONCLUSIONS: We applied JIN to a Vibrio cholerae regulatory network to uncover mechanisms critical to its environmental persistence. V. cholerae is a facultative human pathogen that causes cholera in humans and responsible for seven pandemics. We analyzed the expression response of 17 V. cholerae biofilm indicator genes under various single and multiple knock-outs of three known biofilm regulators. Using the inferred network, we were able to identify new genes involved in biofilm formation more accurately than clustering expression profiles.