Abstract P4-03-01: Pathway level complementarity of germline and somatic events in breast cancer

Background: Progression from a normal cell state to cancer requires multiple genomic hits in key regulatory pathways. In the case of hereditary cancer syndromes, some of these hits occur in the germline, but additional somatic mutations are required for malignant transformation. We hypothesize that this paradigm could be extended to sporadic cancers as well. What somatic mutation function as a cancer driver event may be determined by the constellation of germline variants a person is born with. We propose that even rare, non-recurrent, high functional impact germline variants in genes involved in cancer-related pathways could influence the biological impact of somatic mutations in other cancer-related genes. The goal of the current analysis was to examine associations between pathway alterations caused by high functional impact germline variants or somatic mutations in the “hallmarks of cancer” pathways in breast cancer.

Methods: We obtained germline DNA sequencing and copy number variation (CNV) data from the breast cancer TCGA cohort. After population clustering with the HapMap cohort, we selected a homogeneous group of 796 patients of Western European ancestry and downloaded the matching somatic mutations (SNVs and INDELs) that were available for 750 cases, that comprise the current study population. Germline CNVs were classified as recurrent or rare losses or gains. Potentially pathogenic germline variants (SNPs) were obtained from the PanCancer Altas project. All germline or somatic mutations were mapped at the gene level to the 50 Cancer Hallmarks pathway collection. We designated a pathway mutated if at least 1 gene had a germline or a somatic mutation. Complementarity between pathway alterations by germline and somatic events were evaluated using the Fisher exact test adjusted for multiple comparisons.

Results: At the germline level, 2,057 genes were affected by CNVs (mean 30, range 3-151 genes/patient), and a total of 43 genes carried germline pathogenic SNPs that affected 13.8% of the patients. At the somatic level, we detected 40,881 high functional impact mutations (mean 54.3, range 1-3889 mutations/patient) in 13,080 genes (mean 50.8, range 1-3166 genes/patient). The 50 Cancer Hallmark pathways contained 4386 genes (mean 146.5, range 32-200 genes/pathway), and were mutated in the majority of the patients (85% germline, 93% somatic). Several pathways, such as HEME_METABOLISM, INTERFERON_ALPHA_RESPONSE, and KRAS_SIGNALING, were frequently affected by germline alterations, while the somatic mutations were most frequently involved in the COMPLEMENT, E2F_TARGET, and UV_RESPONSE_UP. Interaction analysis revealed co-occurrence between MYC_TARGETS_V1 (germline) and UV_RESPONSE_DN (somatic) or MTORC1_SINGALING (somatic) (p<0.01), and TNFA_SIGNALING_VIA_NFKB (germline) and IL6_JAK_STAT3_SIGNALING (germline) with E2F_TARGETS (somatic) (p<0.01). We also observed an exclusive relationship between germline alterations in BILE_ACID_METABOLISM and somatic mutations in COMPLEMENT pathway (p<0.01).

Conclusions: Our results highlight the importance of pathway-level analysis of germline alterations in breast cancer, which might help to understand the interrelationship between germline and somatic alterations in breast cancer.

Citation Format: Qing T, Marczyk M, Wali V, Gunasekharan V, Patwardhan G, Pusztai L, Hatzis C. Pathway level complementarity of germline and somatic events in breast cancer [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 P4-03-01.

Abstract P2-06-06: Targeting loss of isoenzyme diversity as a novel therapeutic strategy in breast cancer

Background: Several metabolic steps are mediated by distinct proteins or isoenzymes that catalyze the same reaction, providing redundancy of metabolic functions. Metabolic states are frequently altered in cancer to support survival and proliferation in hypoxic and otherwise hostile microenvironments, and metabolic re-wiring often involve loss of isoenzyme diversity. We hypothesize that targeting enzymes that have lost isoenzyme diversity in cancer, but not in normal cells, provides an opportunity to selectively target cancers. In this study, we assessed mRNA expression of all known human isoenzyme families in breast cancer and normal breast tissue and identified isoenzymes with loss of diversity within each breast cancer subtype.

Methods: We obtained RNAseq data from cancer and patient-matched normal breast tissues from the TCGA (N=66 HR+, N=24 HER2+, and N=15 TNBC tumors). We retrieved annotated human isoenzyme families from the ENZYME nomenclature database. We compared expression in cancer and matched normal samples from the same patient to identify isoenzymes that had i) same or increased expression of the target isoenzyme in cancer vs normal and ii) reduced expression of the complementary isoenzymes in cancer. We developed five scores that capture various elements of these characteristics and prioritized candidates as targets based on clustering and their combined ranking based on the five scores. We validated overexpression of the candidate isoenzymes relative to other isoforms in breast cancer microarray data from ArrayExpress (E-GEOD-76250: 33 TNBC, and E-GEOD-70951: 30 TNBC, 108 HR+, 10 HER2+).

Results: We identified 321 enzymes in the TCGA discovery cohort that correspond to 829 unique isoenzymes. Overall, 636, 483 and 429 isoenzymes were differentially expressed in HR+, HER2+ and TNBC cancers, respectively, compared to corresponding normal samples. Of these, 308 isoenzymes were differentially expressed relative to normal in all 3 subtypes. In all, 112 and 92, and 84 were selected as candidate isoenzyme therapeutic targets in HR+, HER2+ and TNBC, respectively. 23 isoenzymes prioritized in clustering step were further validated. Finally, 6 isoenzymes were validated in HR+ (ALDOA, GUSB, GYG1, MIF, P3H1, PCK2), 10 in HER2+ (ALDH1L2, ALDOA, GLYATL2, GUSB, GYG1, GYS1, MIF, P3H1, PCK2, PTGS1) and 12 in TNBC (ADSS, ALAS1, ALDH1L2, ALDOA, ART3, GLYATL2, GUSB, GYS1, HS3ST1, MIF, PCK2, SOAT1), as potential targets for breast cancer treatment. Of these, 5 potential isoenzyme targets (ALDH1L2, GUSB, GLYATL2, MIF, PCK2), which were mostly hydrolases and transferases, were further selected for ongoing experimental validation in the laboratory. Decreased expression of the complementary isoforms of these 5 targets were primarily due to DNA methylation of the genes in cancer.

Conclusions: We found that loss of isoenzyme diversity is a broad phenomenon in breast cancers that may be explored therapeutically. We identified several instances of “isoenzyme addiction” in which cancers depend exclusively on a single isoenzyme while downregulating via methylation the complementary isoenzymes, providing cancer-specific targeting opportunities. We are currently validating several of these targets in cell line models.

Citation Format: Marczyk M, Gunasekharan V, Wali VB, Shi W, Patwardhan G, Qing T, Pusztai L, Hatzis C. Targeting loss of isoenzyme diversity as a novel therapeutic strategy in breast cancer [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 P2-06-06.

Abstract P4-08-20: Pre-analytical effects of FFPE extraction methods on targeted and whole transcriptome sequencing assays for endocrine sensitivity in metastatic breast cancer

Background: The clinical management of patients with metastatic HR-positive breast cancer is often uncertain due to decreased sensitivity to anti-estrogen therapy over time. Recently, we developed a targeted RNAseq based 18-transcript SET ER/PR assay of endocrine sensitivity from biopsies of metastatic cancer. In this work we assess the effect of pre-analytical factors, specifically RNA extraction methods for FFPE tissue samples, on the reliability of the targeted RNAseq assay.

Methods: FFPE blocks and matched fresh frozen (FF) sections from 12 tumors were collected at MD Anderson Cancer Center. RNA from FFPE slides was extracted in duplicate using three kits (Norgen, Qiagen, Roche), and RNAseq libraries from all samples were prepared using Kapa Total RNAseq kit. Targeted RNA libraries were prepared using droplet-based PCR (RainDance), and also by transcriptome-wide RNAseq for comparison. Reads were mapped to genomic sequence using STAR and expression was quantified using RSEM. Expression data were normalized based on expression of 10 reference genes. The effect of FFPE RNA extraction kit on the reliability of the SET index was assessed using linear mixed effects model (LME) analysis, and agreement with FF was assessed using the concordance correlation coefficient (CCC).

Results: Analysis of the whole transcriptome RNAseq data confirmed minimal 3'-end transcript bias from FFPE samples, irrespective of transcript size or FFPE kit. All 18 genes included in the SET index had high overall concordance between FFPE and FF (median CCC percentile=98.8, range 57.2-99.9 for Norgen; similar for the other two kits) and relatively consistent bias across genes, as estimated by the random effects of the LME model. Furthermore, compared to random 18-gene indices, concordance in the SET index values between FF and FFPE was higher than 99.8% of the random samples, verifying the analytical reliability of the selected genes. For the targeted RNAseq assay, RNA from FFPE extracted with the Norgen kit showed the highest concordance compared to FF (CCC=0.956, 95%CI 0.871-0.985). In general, the analytical variation of SET from FFPE samples was greater than that from FF (1.71-2.71 fold greater), with the lowest variation associated with the Norgen kit. The SET index values from targeted RNAseq for both FF and FFPE samples were consistently lower compared to transcriptome-wide RNAseq but were highly correlated, with the Norgen kit having the highest correlation between targeted and transcriptome-wide RNAseq (rho=0.915).

Conclusions: All three FFPE RNA extraction kits have excellent analytical performance compared to FF samples. The Norgen kit may be marginally better yielding higher concordance with FF and lower analytical variation between replicates. All genes in the SET ER/PR showed very good analytical performance in comparison to random indices and individual genes. Targeted gene RNA sequencing appears very promising as a platform for clinical deployment of quantitative assays, showing only a small (fixable) bias compared to RNAseq.

Citation Format: Marczyk M, Fu C, Lau R, Du L, Trevarton AJ, Sinn BV, Gould RE, Symmans WF, Hatzis C. Pre-analytical effects of FFPE extraction methods on targeted and whole transcriptome sequencing assays for endocrine sensitivity in metastatic breast cancer [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 P4-08-20.