Characterization of a genomic signature of pregnancy identified in the breast.

The objective of this study was to comprehensively compare the genomic profiles in the breast of parous and nulliparous postmenopausal women to identify genes that permanently change their expression following pregnancy. The study was designed as a two-phase approach. In the discovery phase, we compared breast genomic profiles of 37 parous with 18 nulliparous postmenopausal women. In the validation phase, confirmation of the genomic patterns observed in the discovery phase was sought in an independent set of 30 parous and 22 nulliparous postmenopausal women. RNA was hybridized to Affymetrix HG_U133 Plus 2.0 oligonucleotide arrays containing probes to 54,675 transcripts, scanned and the images analyzed using Affymetrix GCOS software. Surrogate variable analysis, logistic regression, and significance analysis of microarrays were used to identify statistically significant differences in expression of genes. The false discovery rate (FDR) approach was used to control for multiple comparisons. We found that 208 genes (305 probe sets) were differentially expressed between parous and nulliparous women in both discovery and validation phases of the study at an FDR of 10% and with at least a 1.25-fold change. These genes are involved in regulation of transcription, centrosome organization, RNA splicing, cell-cycle control, adhesion, and differentiation. The results provide initial evidence that full-term pregnancy induces long-term genomic changes in the breast. The genomic signature of pregnancy could be used as an intermediate marker to assess potential chemopreventive interventions with hormones mimicking the effects of pregnancy for prevention of breast cancer

Characterization of a genomic signature of pregnancy identified in the breast.

The objective of this study was to comprehensively compare the genomic profiles in the breast of parous and nulliparous postmenopausal women to identify genes that permanently change their expression following pregnancy. The study was designed as a two-phase approach. In the discovery phase, we compared breast genomic profiles of 37 parous with 18 nulliparous postmenopausal women. In the validation phase, confirmation of the genomic patterns observed in the discovery phase was sought in an independent set of 30 parous and 22 nulliparous postmenopausal women. RNA was hybridized to Affymetrix HG_U133 Plus 2.0 oligonucleotide arrays containing probes to 54,675 transcripts, scanned and the images analyzed using Affymetrix GCOS software. Surrogate variable analysis, logistic regression, and significance analysis of microarrays were used to identify statistically significant differences in expression of genes. The false discovery rate (FDR) approach was used to control for multiple comparisons. We found that 208 genes (305 probe sets) were differentially expressed between parous and nulliparous women in both discovery and validation phases of the study at an FDR of 10% and with at least a 1.25-fold change. These genes are involved in regulation of transcription, centrosome organization, RNA splicing, cell-cycle control, adhesion, and differentiation. The results provide initial evidence that full-term pregnancy induces long-term genomic changes in the breast. The genomic signature of pregnancy could be used as an intermediate marker to assess potential chemopreventive interventions with hormones mimicking the effects of pregnancy for prevention of breast cancer

Disease Control With FOLFIRI Plus Ziv-aflibercept (zFOLFIRI) Beyond FOLFIRI Plus Bevacizumab: Case Series in Metastatic Colorectal Cancer (mCRC)

Background: The prognosis of patients with metastatic colorectal cancer (mCRC) is poor, especially after failure of initial systemic therapy. The VELOUR study showed modestly prolonged overall survival (OS) with ziv-aflibercept plus 5-fluorouracil, leucovorin, and irinotecan (zFOLFIRI) vs. placebo+FOLFIRI after progression on 5-fluoruracil, leucovorin, and oxaliplatin (FOLFOX) ± bevacizumab. The utility of zFOLFIRI after bevacizumab+FOLFIRI is unknown and not recommended in NCCN guidelines. We explored whether zFOLFIRI may be active beyond progression on bevacizumab+FOLFIRI.Methods: We undertook a retrospective analysis of patients treated in routine clinical practice. A chart review was conducted for a cohort (N = 19) of advanced cancer patients (18 mCRC) who received zFOLFIRI from 2014 to 2018 at Fox Chase Cancer Center (FCCC). Analysis included time on zFOLFIRI, PFS, OS, CEA trends and adverse events. A second mCRC cohort (N = 26) from the Flatiron Health EHR-derived database treated with zFOLFIRI after prior bevacizumab+FOLFOX and bevacizumab+FOLFIRI was analyzed for time-on-treatment and overall survival.Results: Median age of mCRC cohort at zFOLFIRI treatment was 54 (FCCC; N = 18) and 62 (Flatiron Health-cohort; N = 26). Of 18 FCCC mCRC patients, 1 patient had prior bevacizumab+FOLFOX and ramucirumab+irinotecan prior to zFOLFIRI for 8.5 months. Of 17 FCCC mCRC patients with prior bevacizumab+FOLFIRI who received zFOLFIRI, 13 had mutant-KRAS, 3 WT-KRAS, and one BRAF-V600E. The patient with BRAF-V600E mutation achieved stable disease on zFOLFIRI after multiple BRAF-targeted therapies. One patient (WT-KRAS mCRC) remained on zFOLFIRI for 14 months. Of 14 patients with mutated-KRAS, 8 remained on zFOLFIRI for >5 months including 3 for >15 months. The rate-of-change in CEA measures on zFOLFIRI was significantly different (p = 0.004) between rapid progressors and those with PFS>4 months. For mCRC patients treated with zFOLFIRI in the 3rd line or greater (N = 18), median PFS was 7.1 months (214 days) and median OS was 13.8 months (416 days). Median time-on-treatment with zFOLFIRI in the Flatiron Health cohort was 4.4 months, median OS was 7.8 months, and longest time-on-treatment with zFOLFIRI was 266 days.Conclusions: In these small real-world cohorts, clinical meaningful stable disease and overall survival on zFOLFIRI beyond progression on bevacizumab+FOLFIRI was observed in patients with mCRC. Further exploration of this approach is warranted

Clinical Utilization Pattern of Liquid Biopsies (LB) to Detect Actionable Driver Mutations, Guide Treatment Decisions and Monitor Disease Burden During Treatment of 33 Metastatic Colorectal Cancer (mCRC) Patients (pts) at a Fox Chase Cancer Center GI Oncology Subspecialty Clinic

Background: Liquid biopsy (LB) captures dynamic genomic alterations (alts) across metastatic colorectal cancer (mCRC) therapy and may complement tissue biopsy (TB). We sought to describe the utility of LB and better understand mCRC biology during therapy.Methods: Thirty-three patients (pts) with mCRC underwent LB. We used permutation-based t-tests to assess associations between alts, and clinical variables and used Kendall's tau to measure correlations.Results: Of 33 pts, 15 were women; 22 had colon, and the rest rectal cancer. Pts received a median of two lines of therapy before LB. Nineteen pts had limited testing on TB (RAS/RAF/TP53/APC), 11 extended NGS, and 3 no TB. Maxpct and alts correlated with CEA (p < 0.001, respectively). In 3/5 pts with serial LB, CEA correlated with maxpct trend, and CT tumor burden. In 6 pts, mutant RAS was seen in LB and not TB; 5/6 had received anti-EGFR therapy prior to LB, suggesting RAS alts developed post-therapy. In two pts RAS-mutated by TB, no RAS alts were detected on LB; these pts had low disease burden on CT at time of LB that also did not reveal APC or TP53 alts. In six patients who were KRAS wt based on TB, post anti-EGFR LB revealed subclonal KRAS mutations, likely a treatment effect. The median number of alts was higher post anti-EGFR LB (n = 12) vs. anti-EGFR naïve LB (n = 22) (9.5 vs. 5.5, p = 0.059) but not statistically significant. More alts were also noted in post anti-EGFR therapy LB vs. KRAS wt anti-EGFR-naïve LB (n = 6) (9.5 vs. 5) among patients with KRAS wild-type tumors, although the difference was not significant (p = 0.182).Conclusions: LB across mCRC therapy detects driver mutations, monitors disease burden, and identifies sub-clonal alts that reflect drug resistance, tumor evolution, and heterogeneity. Interpretation of LB results is impacted by clinical context

Immune Gene Signature Expression Differs between African American and Caucasian Patients with Renal Cell Carcinoma

BACKGROUND: Predictive immune signatures such as the T-effector, the 26-gene “Renal 101 Immuno signature” and the 18-gene T-cell inflamed gene expression profile were developed in clinical trials enrolling predominantly Caucasians and there is a dearth of literature comparing tumor biology between African American (AA) and Caucasian patients. OBJECTIVE: To compare the immune gene signature expression in AA (n = 55) and Caucasian (n = 457) patients. METHODS: Raw gene expression count data were downloaded from the TCGA KIRC dataset and tumor samples from “white” and “black or AA” patients were selected. The gene expression values of the immune signatures were VST-transformed normalized counts and compared between the groups. RESULTS: There were 457 Caucasian and 55 AA patients in the TCGA. The immune gene expression in all three signatures was significantly lower in AA patients compared to Caucasians (p &lt; 0.05). We validated our findings in an independent dataset using Nanostring Immune Profile Panel. Since the majority of AA tumors in TCGA were stage I (71%), we compared gene expression between stage I AA tumors (n = 39) with stage I Caucasian tumors (n = 220). Once again, the immune gene expression was significantly lower in AA patients compared to Caucasians (p &lt; 0.05), indicating differences in tumor biology between the races. CONCLUSIONS: Low expression of predictive immune gene signatures in AA compared to Caucasian patients indicates a possible difference in the biology of their tumors. Future studies are needed to validate our findings in other datasets and to study the predictive role of these signatures in AA patients.</jats:p

Variable selection in social-environmental data: sparse regression and tree ensemble machine learning approaches

Abstract Background Social-environmental data obtained from the US Census is an important resource for understanding health disparities, but rarely is the full dataset utilized for analysis. A barrier to incorporating the full data is a lack of solid recommendations for variable selection, with researchers often hand-selecting a few variables. Thus, we evaluated the ability of empirical machine learning approaches to identify social-environmental factors having a true association with a health outcome. Methods We compared several popular machine learning methods, including penalized regressions (e.g. lasso, elastic net), and tree ensemble methods. Via simulation, we assessed the methods’ ability to identify census variables truly associated with binary and continuous outcomes while minimizing false positive results (10 true associations, 1000 total variables). We applied the most promising method to the full census data (p = 14,663 variables) linked to prostate cancer registry data (n = 76,186 cases) to identify social-environmental factors associated with advanced prostate cancer. Results In simulations, we found that elastic net identified many true-positive variables, while lasso provided good control of false positives. Using a combined measure of accuracy, hierarchical clustering based on Spearman’s correlation with sparse group lasso regression performed the best overall. Bayesian Adaptive Regression Trees outperformed other tree ensemble methods, but not the sparse group lasso. In the full dataset, the sparse group lasso successfully identified a subset of variables, three of which replicated earlier findings. Conclusions This analysis demonstrated the potential of empirical machine learning approaches to identify a small subset of census variables having a true association with the outcome, and that replicate across empiric methods. Sparse clustered regression models performed best, as they identified many true positive variables while controlling false positive discoveries. </jats:sec

Abstract 1121: Analysis of gene expression changes as a function of PBRM1, BAP1, and SETD2 mutation status clear cell renal cell carcinoma in TCGA tumors

Abstract In addition to VHL, the most commonly mutated clear cell renal cell carcinoma (ccRCC)genes are the chromatin modifiers PBRM1, BAP1, and SETD2. Mutations in these genes predict loss-of-function and presumably exert cancer-related functions through changes in chromatin context and gene expression. Mining publicly available data from The Cancer Genome Anatomy (TCGA) allowed us to correlate mutation status and gene expression profiles, potentially suggesting roles for these mutations in ccRCC. Gene set enrichment analysis (GSEA)reveals that specific signaling and metabolic changes are common for each tumor mutation and may reveal the biologic mechanism of tumor-associated mutations. Work is underway to functionally validate the effect of loss-of-function ccRCC mutations in epigenetic modifiers. Citation Format: Philip H. Abbosh, Ilsiya Ibragimova, Michael Slifker, Paul Cairns. Analysis of gene expression changes as a function of PBRM1, BAP1, and SETD2 mutation status clear cell renal cell carcinoma in TCGA tumors. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1121. doi:10.1158/1538-7445.AM2015-1121</jats:p

Abstract 1412: High EGFR somatic mutation frequency targeting the TK domain in colorectal cancer patients with microsatellite instability (MSI)

Abstract CRCs arise through genetic changes that impact various driver genes and in some tumors increased mutation rate in microsatellite unstable tumors. The hypermutable phenotype associated with microsatellite instability (MSI) results from loss of the mismatch repair system (MMR) activity. MSI is detected in 15% of all CRCs, and such tumors have a better prognosis and different chemotherapeutic outcome patterns, including high clinical benefit from immune checkpoint therapy or relative resistance to 5-FU as compared to microsatellite stable (MSS) tumors. The epidermal growth factor receptor (EGFR) signaling pathway plays an essential role in carcinogenesis of CRCs and is known to be overexpressed in MSI CRCs while highly mutated in Lung cancer. We analyzed the mutation frequency of deregulated pathways including EGFR and its downstream genes KRAS and BRAF in CRCs. We found a significantly elevated EGFR mutation frequency in CRC MSI-H subtype (45.5% vs. 6.5% in MSS CRCs, p&amp;lt;0.0000001). Although KRAS and NRAS are mutated with high frequency in both MSI-H and MSS groups, BRAF corresponding to RTK-RAS pathways is more altered in MSI-H than MSS CRCs (32.67% vs 13.10%; p=0.001), consistent with the known association between MSI-H CRCs and BRAF mutations. We hypothesized that there might be a mutation pattern in EGFR in CRC subtypes that provide a rationale for EGFR-targeted therapy for a subtype of CRC. Of 1104 profiled CRCs in the COSMIC v73 database, somatic EGFR mutations were mapped for 101 MSI-High versus 916 MSS CRCs. EGFR mutations mapped on the protein structure revealed that mutations are mainly targeting tyrosine kinase (TK) domain while the extracellular domain remained mostly wild-type with potential for targeting by anti-EGFR monoclonal antibodies. It is known that downstream genes such as KRAS as well as expression levels of EGFR ligands can affect the sensitivity of CRCs to anti-EGFR therapy. We didn’t detect any difference in mRNA expression level between MSI-H and MSS group but our analysis is indicative of the presence of G12D mutations in a high proportion of KRAS-mutated MSS CRCs, therefore resistance to anti-EGFR antibodies such as cetuximab would be expected. However, EGFR tyrosine kinase inhibitors such as erlotinib and gefitinib could be considered for further testing in patients with MSI-H tumors that have a lower frequency of G12D KRAS mutations and may have mutant EGFR. Citation Format: Safoora Deihimi, Michael Slifker, Eric A. Ross, Wafik S. El-Deiry. High EGFR somatic mutation frequency targeting the TK domain in colorectal cancer patients with microsatellite instability (MSI) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1412. doi:10.1158/1538-7445.AM2017-1412</jats:p