Kalari, Krishna R.

Abstract Breast tumors develop under environmental pressures with phenotypically variant cells generated by mutation and epigenetic changes providing the substrate for clonal selection. Chromosomal mutations are a feature of spontaneous breast tumors in the BALB-neuT mouse model. However, there is little evidence that specific changes in chromosomal structure or ploidy confer selective advantage in these spontaneous tumors. The prominent exception is a consistent loss of chromosome 4, a little understood feature common to several mouse tumors. We previously measured heterozygosity in spontaneous breast tumors from FVB X BALB-neuT F1 mice using Illumina’s Golden Gate assay. In addition to the expected gain and loss of heterozygosity (LOH) evident throughout the genomes of a cohort of spontaneous tumors, the assay also detected wide-spread stochastic pseudo-“LOH” at 600 discrete loci spread throughout the genome in unique patterns among the tumors. Several of these positions were examined by sequence analysis revealing no deviations from the WT sequences, suggesting the detected “LOH” may have been generated by epigenetic modification of DNA which altered sequence detection. While, epigenetically modified DNA templates recapitulated the observed “LOH” signals, no canonical CpG motifs were present in the majority of the 600 loci probed, suggesting that an unusual DNA modification could be responsible for the unexpected wide-spread stochastic structural changes in the breast tumor DNA. We next assessed whether these putative epigenetic changes in DNA structure might impact gene regulation, and have reported that the stochastic pattern observed as “LOH” in DNA was recapitulated in the transcriptomes in unique patterns among the tumors. A direct correlation between the number of “LOH” variants and the down regulation of hundreds of non-polymorphic genes in the transcriptome also was noted. Furthermore, pathway analyses of the genes exhibiting changes in allelic ratios in these tumors revealed significant enrichments within gene networks regulating tissue homeostasis and antigen presentation, providing strong evidence that the perturbations in gene expression translated into selectable tumor cell phenotypes. We now have extended this study to examine the relationship between the magnitude of change in the expression of genes mapping within the pathways regulating tissue homeostasis (Molecular Mechanisms of Cancer). A remarkable feature of the flagged genes is that the magnitude of change in gene expression was not great in each case, yet the biological consequences were strongly reflected in the evolutionary history of the tumors. Importantly, the polymorphisms marking the parental alleles are mostly silent, not altering the structure of the encoded products. Therefore magnitude and timing of gene expression are the likely determinants of phenotypic variation. Each tumor contained several outliers within the pathways regulating tissue homeostasis, suggesting that the integration of multiple small perturbations in the expression of genes comprising functional networks could influence the biology properties of the tumor cells. Overall expression of the loci marked by allelic outliers was significantly below the average expression found among tumors in the cohort highlighting the importance of down regulation of one allele in the establishment of selectable traits. We find a similar direct correlation of multiple small changes in the transcriptome of normal lymphocytes with immune response phenotypes, suggesting this principle of integration of multiple small deviations in gene expression applies widely to the phenotypes of normal cells, tumors, and by extension to organismic traits. Citation Format: Larry R Pease, Sara J Felts, Adam D Scheid, Xiaojia Tang, Krishna R Kalari. Phenotypes of breast tumor cells and normal lymphocytes are determined by the integration of minor changes in expression of multiple genes: A new dimension in quantitative inheritance [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-09-03.

Abstract Background: The combination of cyclin dependent 4/6 kinase inhibitors (CDK4/6i) with endocrine therapy (ET) has resulted in clinically significant improvements in progression-free survival (PFS) and overall survival (OS) in hormone-receptor (HR)-positive metastatic breast cancer (MBC). However, most patients’ disease ultimately progresses on CDK4/6i and ET. Therefore, further research is necessary to understand the mechanisms driving primary and secondary resistance. PROMISE is a multicenter prospective cohort study enrolling women with HR-positive MBC commencing treatment with palbociclib + letrozole (1st line) or palbociclib + fulvestrant (2nd Line). The study provides a comprehensive “omic” assessment of blood, tumor, urine and the fecal microbiome to identify molecular or cellular features associated with primary endocrine resistance (e.g. disease progression ≤ 12 months on treatment) and acquired resistance to CDK 4/6i. Additionally, patient derived xenografts and organoids are created to test new drug strategies designed to overcome resistance to CDK 4/6i and ET. Here, we present initial sequencing results from pretreatment biospecimens collected from PROMISE study participants. Methods: On-study tumor biopsies and blood samples were collected for DNA/RNA sequencing (TempusTM). The analyzed biospecimens were all obtained prior to initiation of palbociclib and ET. We correlated patient clinical characteristics (phenotypes) with molecular data and responses to protocol treatment. The data were analyzed using a series of cutting-edge bioinformatics pipelines for somatic and germline mutations in addition to copy number alterations (CNAs). The study database was locked for analysis on 06/20/2019. Results: We analyzed the somatic single nucleotide variants/INDELs (sSNV/INDEL) profiles across the tumor samples to determine the genes that were least likely to occur as a result of background mutation processes. Twenty-six patients had somatic copy number alterations (sCNA) and/or sSNV/INDEL in at least one of 18 genes with the most significant sSNV/INDEL profiles (p < 0.03) which included clinically and biologically relevant genes. The genes with the most statistically significant sSNV/INDEL mutation profiles were GATA3, PIK3CA, CDH1, and ESR1 (p < 0.0009). We observed a high percentage of tumors with somatic alterations in GATA3 (23% sSNV/INDEL, 15% sCNA), PIK3CA (38%, 12%), CDH1 (19%, 50%) and ESR1 (19%, 58%). ESR1 mutations were more frequent in patients receiving 2nd line treatment. Other frequently altered genes included TP53 (15%, 46%), MAP2K4 (8%, 50%), DNAAF1 (8%, 50%), and CDKN1B (8%, 35%). Further, ZNF317 and F3 were altered in 9 and 7 patients, respectively. Twenty-four samples had alterations in at least one of the CDK4/6 pathway genes (RB1, CCNE2, CCND1, CDK6, ESR1, CDKN2A, CCND3, CDK4, CDK2 and CCNE1). Four patients progressed on therapy; three of the four patients had mutations in PIK3CA, and one had a mutation in ESR1. Results of the RNA sequencing data (N=26) will be presented at the SABCS meeting. Conclusions: This is the first report of a prospective study designed to characterize the genomic landscape of ER+/HER2- MBC prior to palbociclib treatment. We observed high frequencies of known targetable alterations in PIK3CA and ESR1, including in patients that progress, which is consistent with previous reports. RNA sequencing data will be presented at the meeting. Citation Format: Ciara C O Sullivan, Krishna R Kalari, Vera J Suman, Peter T Vedell, Ann Moyer, Erin Carlson, Jason Sinnwell, Tejaswi Alaparthi, Xiaojia Tang, Kevin Thompson, Jaeyun Sung, Alvaro Moreno-Aspitia, Donald Northfelt, Minetta C Liu, Tufia C Haddad, Prema Peethambaram, Saranya Chumsri, Kathryn J Ruddy, Karthik V Giridhar, Roberto A Leon-Ferre, Paula Gill, Mohammad Ranginwala, Asad Javed, Sameer Batoo, Brendan P. McMenomy, Richard Weinshilboum, Liewei Wang, Matthew P Goetz. Comprehensive tumor sequencing to identify biomarkers of palbociclib response: First report of the PROMISE study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-11-07.

1037 Background: Folate receptor alpha (FRA the product of the FOLR1 gene) has been identified as a potential prognostic and therapeutic target in a number of cancers. A correlation has been shown between intense expression of FRA in breast tumors and poor prognosis, yet little is known about FOLR1/FRA expression across clinically relevant breast cancer subtypes. Methods: 131 breast cancer tumors including 4 benign, 33 ER+, 26 HER2+, and 68 triple negative (TN) were constructed into tissue microarrays (TMAs). FRA expression was analyzed by immunohistochemistry (IHC) using a high affinity FRA antibody. Tumor membrane staining intensity was scored by a pathologist as negative (0), weak (1+), moderate (2+) and strong (3+). The percent of cells within each tissue core stained at each intensity was recorded to calculate an H-score. The H-score is a weighted score that captured both the proportion of positive staining and intensity for each tumor. H-score values can range from zero (no membrane staining) to a maximum of 300 (100% membrane staining at 3+). H-scores for each patient sample were averaged over 3 TMA cores. The mean H-scores for each tumor subtype and the percentage of 3+ staining in >30% of tumor cells were compared by a Mann-Whitney test. The distribution of FOLR1 mRNA was completed using a TCGA RNA-seq dataset from 691 breast tumors classified as ER+, HER2+ and TN. FOLR1 levels of TN versus ER+ and HER2+ were compared by a Mann-Whitney test. Results: The mean H-score for the benign tumors was 0, ER+ (13.31), HER2+ (39.36), TN (119.02). The median H-score for the benign tumors was 0, ER+ (0), HER2+ (7.5), TN (127.5). The TN tumors mean and median H scores were significantly higher than benign, ER+ or HER2+ (p<0.001). The largest percentage of 3+ staining in >30% of tumor cells was observed in TN tumors (36.7%) and lowest in ER+ tumors (0%) (p<0.001). TN tumors had significantly higher levels of FOLR1 mRNA compared to ER+ and HER2+ subtypes (p<0.0001). Conclusions: Our data indicate that expression of FRA is highly prevalent in TN tumors and is supported by FOLR1 mRNA levels. Anti-FRA therapy may represent an important therapeutic intervention in TNBC who to this point have no active targeted treatment options.

Abstract Purpose: To determine if the degree of estrogen suppression with aromatase inhibitors (AI: anastrozole, exemestane, letrozole) is associated with efficacy in early-stage breast cancer, and to examine for differences in the mechanism of action between the three AIs. Experimental Design: Matched case–control studies [247 matched sets from MA.27 (anastrozole vs. exemestane) and PreFace (letrozole) trials] were undertaken to assess whether estrone (E1) or estradiol (E2) concentrations after 6 months of adjuvant therapy were associated with risk of an early breast cancer event (EBCE). Preclinical laboratory studies included luciferase activity, cell proliferation, radio-labeled ligand estrogen receptor binding, surface plasmon resonance ligand receptor binding, and nuclear magnetic resonance assays. Results: Women with E1 ≥1.3 pg/mL and E2 ≥0.5 pg/mL after 6 months of AI treatment had a 2.2-fold increase in risk (P = 0.0005) of an EBCE, and in the anastrozole subgroup, the increase in risk of an EBCE was 3.0-fold (P = 0.001). Preclinical laboratory studies examined mechanisms of action in addition to aromatase inhibition and showed that only anastrozole could directly bind to estrogen receptor α (ERα), activate estrogen response element-dependent transcription, and stimulate growth of an aromatase-deficient CYP19A1−/− T47D breast cancer cell line. Conclusions: This matched case–control clinical study revealed that levels of estrone and estradiol above identified thresholds after 6 months of adjuvant anastrozole treatment were associated with increased risk of an EBCE. Preclinical laboratory studies revealed that anastrozole, but not exemestane or letrozole, is a ligand for ERα. These findings represent potential steps towards individualized anastrozole therapy.

Single nucleotide variants in the open reading frames (ORFs) of pharmacogenes are important causes of interindividual variability in drug response. The functional characterization of variants of unknown significance within ORFs remains a major challenge for pharmacogenomics. Deep mutational scanning (DMS) is a high‐throughput technique that makes it possible to analyze the functional effect of hundreds of variants in a parallel and scalable fashion. We adapted a “landing pad” DMS system to study the function of missense variants in the ORFs of cytochrome P450 family 2 subfamily C member 9 ( CYP2C9 ) and cytochrome P450 family 2 subfamily C member 19 ( CYP2C19 ). We studied 230 observed missense variants in the CYP2C9 and CYP2C19 ORFs and found that 19 of 109 CYP2C9 and 36 of 121 CYP2C19 variants displayed less than ~ 25% of the wild‐type protein expression, a level that may have clinical relevance. Our results support DMS as an efficient method for the identification of damaging ORF variants that might have potential clinical pharmacogenomic application.

Abstract Purpose: We hypothesized that integrated analysis of cancer types from different lineages would reveal novel molecularly defined subgroups with unique therapeutic vulnerabilities. On the basis of the molecular similarities between subgroups of breast and ovarian cancers, we analyzed these cancers as a single cohort to test our hypothesis. Experimental Design: Identification of transcriptional subgroups of cancers and drug sensitivity analyses were performed using mined data. Cell line sensitivity to Hsp90 inhibitors (Hsp90i) was tested in vitro. The ability of a transcriptional signature to predict Hsp90i sensitivity was validated using cell lines, and cell line- and patient-derived xenograft (PDX) models. Mechanisms of Hsp90i sensitivity were uncovered using immunoblot and RNAi. Results: Transcriptomic analyses of breast and ovarian cancer cell lines uncovered two mixed subgroups comprised primarily of triple-negative breast and multiple ovarian cancer subtypes. Drug sensitivity analyses revealed that cells of one mixed subgroup are significantly more sensitive to Hsp90i compared with cells from all other cancer lineages evaluated. A gene expression classifier was generated that predicted Hsp90i sensitivity in vitro, and in cell line- and PDXs. Cells from the Hsp90i-sensitive subgroup underwent apoptosis mediated by Hsp90i-induced upregulation of the proapoptotic proteins Bim and PUMA. Conclusions: Our findings identify Hsp90i as a potential therapeutic strategy for a transcriptionally defined subgroup of ovarian and breast cancers. This study demonstrates that gene expression profiles may be useful to identify therapeutic vulnerabilities in tumor types with limited targetable genetic alterations, and to identify molecularly definable cancer subgroups that transcend lineage.

Abstract Motivation Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform read distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide bias correction step(s), which is based on biological considerations—such as GC content—and applied in single samples separately. The main problem is that not all biases are known. Results We have developed a novel method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xβ, where the design matrix X is known and is computed based on the simplifying assumptions. In contrast XAEM considers Xβ as a bilinear model with both X and β unknown. Joint estimation of X and β is made possible by a simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. We use an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and β. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes. In a differential-expression analysis of a real single-cell RNA-seq dataset, XAEM achieves substantially better rediscovery rates in independent validation sets. Availability and implementation The method and pipeline are implemented as a tool and freely available for use at http://fafner.meb.ki.se/biostatwiki/xaem/. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract Molecular and cellular diversity in cancer cells enables evolutionary processes that govern tumor progression and contributes to treatment failures, but the mechanisms governing this variability remain incompletely understood. We previously described tumor-specific epigenetic alterations in DNA devoid of canonical CpG methylation sites throughout the genomes of spontaneous breast tumors which arise in genetically uniform, yet heterozygous, [FVB X BALB/c MMTV-NeuT+] F1 mice. Those changes were detected in purified tumor DNA using a template-assisted quantitative ligation assay employing locus-specific and SNP-specific oligonucleotide genotyping probes, and the assay was subsequently shown to be sensitive to epigenetic modifications of the templates. We now describe losses of heterozygosity similarly distributed throughout the transcriptomes of tumors in this same model. We quantified expressed alleles across a population of clonal tumors and tested each expressed variant for the occurrence of allelic ratio outliers. The results revealed variegated patterns of expression in hundreds of genes, highly significantly enriched in pathways typically co-opted by tumors (Molecular Mechanisms of Cancer, P &lt; 10-10, IPA analysis). An astounding 87% (2634 of 3044) of expressed genes that could be measured using this approach were represented in at least one tumor. Furthermore, the frequency of these outliers in any one individual tumor was found to correlate strongly with the transcriptional repression of an additional large set of non-polymorphic genes. These findings reveal underappreciated, latent mechanisms driving sporadic errors in epigenetic programming that promote repression of a multiple cis-linked transcripts. This genome-wide molecular chaos presents as dysregulated cellular homeostasis and is the foundation for heritable diversity as tumors evolve. Citation Format: Sara J. Felts, Xiaojia Tang, Virginia P. Van Keulen, Krishna R. Kalari, Larry R. Pease. Stochastic variation in gene expression is selected during clonal evolution of spontaneous mouse mammary tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2898.