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Purpose: The goal of this study was to determine whether distinct gene expression profiles are associated with intrinsic and/or acquired chemoresistance in epithelial ovarian carcinoma.Experimental Design: Gene expression profiles were generated from 21 primary chemosensitive tumors and 24 primary chemoresistant tumors using cDNA-based microarrays. Gene expression profiles of both groups of primary tumors were then compared with those of 15 ovarian carcinomas obtained following platinum-based chemotherapy (“postchemotherapy” tumors). A theme discovery tool was used to identify functional categories of genes involved in drug resistance.Results: Comparison of primary chemosensitive and chemoresistant tumors revealed differential expression of 85 genes (P < 0.001). Comparison of gene expression profiles of primary chemosensitive tumors and postchemotherapy tumors revealed more robust differences with 760 genes differentiating the two groups (P < 0.001). In contrast, only 230 genes were differentially expressed between primary chemoresistant and postchemotherapy groups (P < 0.001). Common to both gene lists were 178 genes representing transcripts differentially expressed between postchemotherapy tumors and all primary tumors irrespective of intrinsic chemosensitivity. The gene expression profile of postchemotherapy tumors compared with that of primary tumors revealed statistically significant overrepresentation of genes encoding extracellular matrix–related proteins.Conclusions: These data show that gene expression profiling can discriminate primary chemoresistant from primary chemosensitive ovarian cancers. Gene expression profiles were also identified that correlate with states of intrinsic and acquired chemoresistance and that represent targets for future investigation and potential therapeutic interventions.

Supplemental Figure 3. COO correlations across platforms

Patient sample details

Supplementary Figure S1 from Gene Expression Profiles Associated with Response to Chemotherapy in Epithelial Ovarian Cancers

Purpose:Common resistance mechanisms to endocrine therapy (ET) in estrogen receptor (ER)–positive metastatic breast cancers include, among others, ER loss and acquired activating mutations in the ligand-binding domain of the ER gene (ESR1LBDm). ESR1 mutational mediated resistance may be overcome by selective ER degraders (SERD). During the first-in-human study of oral SERD AZD9496, early changes in circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) were explored as potential noninvasive tools, alongside paired tumor biopsies, to assess pharmacodynamics and early efficacy.Experimental Design:CTC were enumerated/phenotyped for ER and Ki67 using CellSearch in serial blood draws. ctDNA was assessed for the most common ESR1LBDm by droplet digital PCR (BioRad).Results:Before starting AZD9496, 11 of 43 (25%) patients had ≥5 CTC/7.5 mL whole blood (WB), none of whom underwent reduction to +, two of whom had CTC-ER+ reduction. CTC-Ki67 status did not change appreciably. Patients with ≥5 CTC/7.5 mL WB before treatment had worse progression-free survival (PFS) than patients with P = 0.0003). Fourteen of 45 (31%) patients had ESR1LBDm+ ctDNA at baseline, five of whom had ≥2 unique mutations. Baseline ESR1LBDm status was not prognostic. Patients with persistently elevated CTC and/or ESR1LBDm+ ctDNA at C1D15 had worse PFS than patients who did not (P = 0.0007).Conclusions:Elevated CTC at baseline was a strong prognostic factor in this cohort. Early on-treatment changes were observed in CTC-ER+ and ESR1LBDm+ ctDNA, but not in overall CTC number. Integrating multiple biomarkers in prospective trials may improve outcome prediction and ET resistance mechanisms' identification over a single biomarker.

Purpose: To develop a clinically viable gene expression assay to measure RAS/RAF/MEK/ERK (RAS–ERK) pathway output suitable for hypothesis testing in non–small cell lung cancer (NSCLC) clinical studies.Experimental Design: A published MEK functional activation signature (MEK signature) that measures RAS–ERK functional output was optimized for NSCLC in silico. NanoString assays were developed for the NSCLC optimized MEK signature and the 147-gene RAS signature. First, platform transfer from Affymetrix to NanoString, and signature modulation following treatment with KRAS siRNA and MEK inhibitor, were investigated in cell lines. Second, the association of the signatures with KRAS mutation status, dynamic range, technical reproducibility, and spatial and temporal variation was investigated in NSCLC formalin-fixed paraffin-embedded tissue (FFPET) samples.Results: We observed a strong cross-platform correlation and modulation of signatures in vitro. Technical and biological replicates showed consistent signature scores that were robust to variation in input total RNA; conservation of scores between primary and metastatic tumor was statistically significant. There were statistically significant associations between high MEK (P = 0.028) and RAS (P = 0.003) signature scores and KRAS mutation in 50 NSCLC samples. The signatures identify overlapping but distinct candidate patient populations from each other and from KRAS mutation testing.Conclusions: We developed a technically and biologically robust NanoString gene expression assay of MEK pathway output, compatible with the quantities of FFPET routinely available. The gene signatures identified a different patient population for MEK inhibitor treatment compared with KRAS mutation testing. The predictive power of the MEK signature should be studied further in clinical trials. Clin Cancer Res; 23(6); 1471–80. ©2016 AACR.See related commentary by Xue and Lito, p. 1365

Patient tumor gene expression data

Supplementary Table S1 from Accelerated Preclinical Testing Using Transplanted Tumors from Genetically Engineered Mouse Breast Cancer Models

Purpose:PIK3CA mutations are frequent in breast cancer and activate the PI3K/Akt pathway. Unexpectedly, PIK3CA mutation appears in general to be associated with better outcome. In a cohort of patients where both primary and metastatic lesions were available, the objective was to assess changes in PIK3CA mutations. We wished to discern whether selective pressures occur and the influence of PIK3CA mutation on time to recurrence.Experimental Design: Formalin-fixed paraffin-embedded tumor blocks were obtained from 104 patients with paired samples from primary tumors and corresponding asynchronous metastatic breast tumors. Samples were analyzed for PIK3CA mutations (exons 9 and 20) as well as immunohistochemical evaluation for PTEN, pAKT, Ki67, ER, and HER2.Results:PIK3CA mutation was detected in 45% of the primary tumors. Overall, there was a net gain in mutation in metastatic disease, to 53%; nonetheless, there were instances where metastases were wild type in patients with PIK3CA mutant primary tumors. Laser capture microdissection on a subset of cases revealed microheterogeneity for PIK3CA mutational status in the primary tumor. PIK3CA mutants overall showed a significantly longer time to first recurrence than wild type cases (P = 0.03).Conclusion:PIK3CA mutations occur at high frequency in primary and metastatic breast cancer; these may not necessarily confer increased aggressiveness as mutants had a longer time to recurrence. Because PIK3CA status quite frequently changes between primary and metastatic disease, it emphasizes the necessity of assessing the PIK3CA status in the metastatic lesion for selection of PIK3CA inhibitor therapy. Clin Cancer Res; 17(4); 667–77. ©2010 AACR.

Purpose:Danvatirsen is a therapeutic antisense oligonucleotide (ASO) that selectively targets STAT3 and has shown clinical activity in two phase I clinical studies. We interrogated the clinical mechanism of action using danvatirsen-treated patient samples and conducted back-translational studies to further elucidate its immunomodulatory mechanism of action.Experimental Design:Paired biopsies and blood samples from danvatirsen-treated patients were evaluated using immunohistochemistry and gene-expression analysis. To gain mechanistic insight, we used mass cytometry, flow cytometry, and immunofluorescence analysis of CT26 tumors treated with a mouse surrogate STAT3 ASO, and human immune cells were treated in vitro with danvatirsen.Results:Within the tumors of treated patients, danvatirsen uptake was observed mainly in cells of the tumor microenvironment (TME). Gene expression analysis comparing baseline and on-treatment tumor samples showed increased expression of proinflammatory genes. In mouse models, STAT3 ASO demonstrated partial tumor growth inhibition and enhanced the antitumor activity when combined with anti–PD-L1. Immune profiling revealed reduced STAT3 protein in immune and stromal cells, and decreased suppressive cytokines correlating with increased proinflammatory macrophages and cytokine production. These changes led to enhanced T-cell abundance and function in combination with anti–PD-L1.Conclusions:STAT3 ASO treatment reverses a suppressive TME and promotes proinflammatory gene expression changes in patients' tumors and mouse models. Preclinical data provide evidence that ASO-mediated inhibition of STAT3 in the immune compartment is sufficient to remodel the TME and enhance the activity of checkpoint blockade without direct STAT3 inhibition in tumor cells. Collectively, these data provide a rationale for testing this combination in the clinic.

Supplemental Table 2. Identification of BCL2 translocation events

Figure S1: Downregulation of CCR2, CD180, MYC and upregulation of HEXIM1 mRNAs by AZD5153 in cells can be detected at 4h; Figure S2: Other BRD4/BET compounds can also downregulate CCR2 and CD180 mRNAs in hematological tumor cell lines; Supplementary Table 1: Description of cell lines used in the RNAseq study; Supplementary Table 2: Information on qPCR assays used in these studies; Supplementary Table 3: More detailed information for each healthy volunteer and patient sample.

Supplementary Figure Legend from Accelerated Preclinical Testing Using Transplanted Tumors from Genetically Engineered Mouse Breast Cancer Models

Purpose: The presence of similar histologic subtypes of epithelial ovarian and endometrial cancers has long been noted, although the relevance of this finding to pathogenesis and clinical management is unclear. Despite similar clinical characteristics, histologic subtypes of cancers of the ovary and endometrium are treated according to organ of origin. This study compares the gene expression profiles of analogous histologic subtypes of cancers of the ovary and endometrium using the same genomic platform to determine the similarities and differences between these tumors.Experimental Design: Gene expression profiles of 75 cancers (endometrioid, serous, and clear cell) of the ovary and endometrium, five renal clear cell cancers, and seven normal epithelial brushings were determined using a 11,000-element cDNA array. All images were analyzed using BRB ArrayTools. Validation was done using real-time PCR on select genes and immunohistochemical staining.Results: Comparison across endometrial and ovarian cancers and serous and endometrioid tumors showed expression patterns reflecting their organ of origin. Clear cell tumors, however, showed remarkably similar expression patterns regardless of their origin, even when compared with renal clear cell samples. A set of 43 genes was common to comparisons of each of the three histologic subtypes of ovarian cancer with normal ovarian surface epithelium.Conclusions: The comparison of the gene expression profiles of endometrioid and serous subtypes of ovarian and endometrial cancer are largely unique to the combination of a particular subtype in a specific organ. In contrast, clear cell cancers show a remarkable similarity in gene expression profiles across organs (including kidney) and could not be statistically distinguished.

Cytokine, T, and NK cell changes in CT26 tumors

Gating and antibodies for flow cytometry and CyTOF

Article Figures 1-4 from Gene Expression Profiles Associated with Response to Chemotherapy in Epithelial Ovarian Cancers

Supplementary Tables S1-S4.

Supplemental Figure 4. Comparison of NanoString codeset with Scott et al

Supplementary Figure S1 from Global Expression Analysis of Cancer/Testis Genes in Uterine Cancers Reveals a High Incidence of BORIS Expression

Purpose:The abundance and effects of structural variation at BRCA1/2 in tumors are not well understood. In particular, the impact of these events on homologous recombination repair deficiency (HRD) has yet to be demonstrated.Experimental Design:Exploiting a large collection of whole-genome sequencing data from high-grade serous ovarian carcinoma (N = 205) together with matched RNA sequencing for the majority of tumors (N = 150), we have comprehensively characterized mutation and expression at BRCA1/2.Results:In addition to the known spectrum of short somatic mutations (SSM), we discovered that multi-megabase structural variants (SV) were a frequent, unappreciated source of BRCA1/2 disruption in these tumors, and we found a genome-wide enrichment for large deletions at the BRCA1/2 loci across the cohort. These SVs independently affected a substantial proportion of patients (16%) in addition to those affected by SSMs (24%), conferring HRD and impacting patient survival. We also detail compound deficiencies involving SSMs and SVs at both loci, demonstrating that the strongest risk of HRD emerges from combined SVs at both BRCA1 and BRCA2 in the absence of SSMs. Furthermore, these SVs are abundant and disruptive in other cancer types.Conclusions:These results extend our understanding of the mutational landscape underlying HRD, increase the number of patients predicted to benefit from therapies exploiting HRD, and suggest there is currently untapped potential in SV detection for patient stratification.

Supplementary methods and supplementary figures

Supplemental Figure 2. COO designations across cell lines

Cav-1 Figure S4 from Gene Expression Profiles of Serous, Endometrioid, and Clear Cell Subtypes of Ovarian and Endometrial Cancer

qRT-PCR Validation Figures S5 & S6 from Gene Expression Profiles of Serous, Endometrioid, and Clear Cell Subtypes of Ovarian and Endometrial Cancer

Supplemental Table 1: Design of Affymetrix and NanoString probes Supplemental Table 2: Genes included in the NanoString Codeset, including genes up- and down-regulated by MEK, Cres and KRAS signatures, plus housekeeper genes Supplemental Table 3: Correlation between Affymetrix and NanoString data for Batch Comparison or New probes Supplemental Table 4: Pearson's correlation coefficients for MEK and Cres gene probes in NSCLC FFPET Supplemental Fig 1: The MEK signature is dynamic in lung cancer cell lines as demonstrated by MEK inhibition with selumetinib and siRNA mediated KRAS knockdown Supplemental Fig 2: Pearson's correlation coefficients for the most correlated MEK and Cres probes in NSCLC FFPET Supplemental Fig 3: NanoString gene expression range of MEK and CRes genes Supplemental Fig 4: Scatterplots showing correlations between MEK, Cres and RAS signatures Supplemental Fig 5: Pearson's correlations demonstrated a high agreement of MEK signature expression between gene probe batches

Supplemental Table Legends and Supplemental Figure Legends

Supplementary Figure 1. Mutational analysis of TP53 in the olaparib arm. (A) Analysis of TP53 mutation type. (B) BRCA1 and BRCA2 mutation type Supplementary Table 1. Platinum sensitivity and outcome on olaparib maintenance treatment. Supplementary Table 2. Mutational analysis of the FoundationOne® panel from the LT and ST responders in the olaparib arm. Supplementary Table 3. Mutational analysis of PTEN in the olaparib and placebo arms. Supplementary Table 4. Analysis of BRCA1, BRCA2, and TP53 mutation frequency in olaparib patients in the LT and ST responder group.

Supplementary methods and results Supplementary Figure 1. Study 1 (NCT01226316) design Supplementary Figure 2. Dose escalation and DLTs in Part A, with MTDs of the continuous, 4/7, and 2/7 schedules being 320, 480, and 640 mg bid, respectively Supplementary Figure 3. Exploratory mutation analyses (ddPCR) in archival tissue and plasma (ctDNA) samples from a breast cancer patient who had progressive disease as best response (Cb cohort) Supplementary Figure 4. A) Tumour growth inhibition in preclinical tumor xenograft models (BT474c, HGC-27, HCC1954 and 786.0) treated with AZD5363 bid po at the indicated doses. B) AZD5363 PD in BT474c tumor xenografts. C) AZD5363 PD at steady state in BT474c xenograft tumors. D) Time course of pPRAS40 and pAkt S473 in BT474c tumor xenograft tissue following the final dose of AZD5363 after 3 weeks' continuous (100 mg/kg bid) or intermittent dosing (130 mg/kg bid 4/7 and 170 mg/kg bid 2/7) Supplementary Figure 5. Glucose (non-fasting/random) values over time after 480 mg single-dose AZD5363 (all dosing schedules) Supplementary Table 1. Percentage change from baseline for PoM biomarkers for 12 evaluable patients Supplementary Table 2. Antibodies and conditions for IHC analysis Supplementary Table 3. ddPCR primers and probes Supplementary Table 4. Tables with results of molecular analyses of Part C patients (PIK3CA and ESR1 mutation status in tissue and ctDNA, PTEN status in archival tissue) Supplementary Table 5. AEs of CTCAE grade {greater than or equal to}3 (irrespective of causality) in Parts A and B (frequency >3% total) and Part C (frequency >5% total)

Supplementary Table S1 from Global Expression Analysis of Cancer/Testis Genes in Uterine Cancers Reveals a High Incidence of BORIS Expression

Purpose: This phase I, open-label study (Study 1, D3610C00001; NCT01226316) was the first-in-human evaluation of oral AZD5363, a selective pan-AKT inhibitor, in patients with advanced solid malignancies. The objectives were to investigate the safety, tolerability, and pharmacokinetics of AZD5363, define a recommended dosing schedule, and evaluate preliminary clinical activity.Experimental Design: Patients were aged ≥18 years with World Health Organization (WHO) performance status of 0 to 1. Dose escalation was conducted within separate continuous and intermittent [4 days/week (4/7) or 2 days/week (2/7)] schedules with safety, pharmacokinetic, and pharmacodynamic analyses. Expansion cohorts of approximately 20 patients each explored AZD5363 activity in PIK3CA-mutant breast and gynecologic cancers.Results: MTDs were 320, 480, and 640 mg for continuous (n = 47), 4/7 (n = 21), and 2/7 (n = 22) schedules, respectively. Dose-limiting toxicities were rash and diarrhea for continuous, hyperglycemia for 2/7, and none for 4/7. Common adverse events were diarrhea (78%) and nausea (49%) and, for Common Terminology Criteria for Adverse Events grade ≥3 events, hyperglycemia (20%). The recommended phase II dose (480 mg bid, 4/7 intermittent) was assessed in PIK3CA-mutant breast and gynecologic expansion cohorts: 46% and 56% of patients, respectively, showed a reduction in tumor size, with RECIST responses of 4% and 8%. These responses were less than the prespecified 20% response rate; therefore, the criteria to stop further recruitment to the PIK3CA-mutant cohort were met.Conclusions: At the recommended phase II dose, AZD5363 was well tolerated and achieved plasma levels and robust target modulation in tumors. Proof-of-concept responses were observed in patients with PIK3CA-mutant cancers treated with AZD5363. Clin Cancer Res; 24(9); 2050–9. ©2017 AACR.See related commentary by Costa and Bosch, p. 2029

Supplementary Table 1 and Figure Legends from Conditional Expression of the CTCF-Paralogous Transcriptional Factor BORIS in Normal Cells Results in Demethylation and Derepression of MAGE-A1 and Reactivation of Other Cancer-Testis Genes

Supplementary Figure 5 from A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer

Figure 4, Tables 4-8, supplementary text

Supplementary Table S5A from Cancers as Wounds that Do Not Heal: Differences and Similarities between Renal Regeneration/Repair and Renal Cell Carcinoma

Correlation scatter plot of normalised log2 counts of UHR sample tested at 100ng in input volume of 5ul versus 100ng in input volume of 15ul with a CodeSet consisting of 463 including 18 housekeeping genes.

Supplementary Table 4 from A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer

Brother of the Regulator of Imprinted Sites (BORIS) is a mammalian CTCF paralog with the same central 11Zn fingers (11ZF) that mediate specific interactions with varying ∼50-bp target sites. Regulated in vivo occupancy of such sites may yield structurally and functionally distinct CTCF/DNA complexes involved in various aspects of gene regulation, including epigenetic control of gene imprinting and X chromosome inactivation. The latter functions are mediated by meCpG-sensitive 11ZF binding. Because CTCF is normally present in all somatic cells, whereas BORIS is active only in CTCF- and 5-methylcytosine–deficient adult male germ cells, switching DNA occupancy from CTCF to BORIS was suggested to regulate site specificity and timing of epigenetic reprogramming. In addition to 11ZF-binding paternal imprinting control regions, cancer-testis gene promoters also undergo remethylation during CTCF/BORIS switching in germ cells. Only promoters of cancer testis genes are normally silenced in all somatic cells but activated during spermatogenesis when demethylated in BORIS-positive germ cells and are found aberrantly derepressed in various tumors. We show here that BORIS is also expressed in multiple cancers and is thus itself a cancer-testis gene and that conditional expression of BORIS in normal fibroblasts activates cancer-testis genes selectively. We tested if replacement of CTCF by BORIS on regulatory DNA occurs in vivo on activation of a prototype cancer-testis gene, MAGE-A1. Transition from a hypermethylated/silenced to a hypomethylated/activated status induced in normal cells by 5-aza-2′-deoxycytidine (5-azadC) was mimicked by conditional input of BORIS and is associated with complete switching from CTCF to BORIS occupancy at a single 11ZF target. This site manifested a novel type of CTCF/BORIS 11ZF binding insensitive to CpG methylation. Whereas 5-azadC induction of BORIS takes only few hours, derepression of MAGE-A1 occurred 1 to 2 days later, suggesting that BORIS mediates cancer-testis gene activation by 5-azadC. Indeed, infection of normal fibroblasts with anti-BORIS short hairpin RNA retroviruses before treatment with 5-azadC blocked reactivation of MAGE-A1. We suggest that BORIS is likely tethering epigenetic machinery to a novel class of CTCF/BORIS 11ZF target sequences that mediate induction of cancer-testis genes.

Supplementary Figure 4 from Melanoma Antigen-11 Inhibits the Hypoxia-Inducible Factor Prolyl Hydroxylase 2 and Activates Hypoxic Response

Supplementary Figure 2 from Conditional Expression of the CTCF-Paralogous Transcriptional Factor BORIS in Normal Cells Results in Demethylation and Derepression of MAGE-A1 and Reactivation of Other Cancer-Testis Genes

Supplemental Figure 1A: Variability due to number of FOV captured. A total of 287 genes were ranked on expression level and grouped as LOW, MEDIUM or HIGH expression for each FOV category. The Standard Deviation (SD) is calculated from mean of 3 fresh clinical DLBCL samples. A sample was determined below LOD if one or both technical replicates were Not-Detected. Data was normalised against 33 housekeeping genes. Supplemental Figure 1B: Reduction in raw counts observed after re-scanning the same cartridge at 280 FOV for a total number of 10 times. The mean % reduction in raw counts of the top positive control spike of 5 independent samples (4 FFPE prostate and UHR) is illustrated. The CodeSet size used was 516 genes including 9 housekeeping genes. Supplemental Figure 1C: Variability due to Cartridge Lane and Analyser Station Slot Position; A human Reference RNA sample was replicated across the 12 lanes of the same cartridge. The cartridge was read at 25 FOV in each of the 6 slots in the Analyser Station. The normalised log2 expression values of a HIGH (mean = 18.1{plus minus}0.14) and a LOW abundance gene (mean = 10.1{plus minus}0.40) are illustrated from a CodeSet consisting of 48 housekeeping genes. Data was normalised against 2 randomly selected housekeeping genes. Supplemental Figure 1D: Correlation scatter plot of normalised log2 counts of representative gastric FFPE sample replicated at 100ng on nCounter GEN1 and GEN2 Prep Stations. Only genes above the LOD are shown from a CodeSet consisting of 506 genes including 8 housekeeping genes.

Supplementary Figure 3 from A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer

Supplementary Figure 1 from Chromosome Transfer Induced Aneuploidy Results in Complex Dysregulation of the Cellular Transcriptome in Immortalized and Cancer Cells

Supplementary Figure 1 from Melanoma Antigen-11 Inhibits the Hypoxia-Inducible Factor Prolyl Hydroxylase 2 and Activates Hypoxic Response

Supplementary Figure 1 from A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer

Supplementary Table S2 from Role of ETS Transcription Factors in the Hypoxia-Inducible Factor-2 Target Gene Selection

Correlation scatter plot of a representative lung cell line (NCI-H520) showing correlation of 118 overlapping genes in non-identical Codesets. An unusual outlier is marked with a circle. Data was normalised against 8 housekeeping genes.

Supplementary Table 2 from A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer

Supplementary Methods from Chromosome Transfer Induced Aneuploidy Results in Complex Dysregulation of the Cellular Transcriptome in Immortalized and Cancer Cells

Differentially Regulated Gene List from Expression Profiling of Serous Low Malignant Potential, Low-Grade, and High-Grade Tumors of the Ovary