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Supplementary Figure 1. AZD5153 shows reduced BRD4 binding activity when BD2 function is abolished; Supplementary Figure 2. AZD5153 modulates MYC protein levels across hematologic cancer cell lines; Supplementary Figure 3. Gene Ontology (GO) analysis of the down-regulated transcripts by AZD5153 across all cell line types; Supplementary Figure 4. IC50 and percent maximal cell kill by AZD5153 and I-BET762 in five hematologic cell lines; Supplementary Figure 5. Tumor growth inhibition across six hematological xenograft Models

55 FGFR2 modified dynamic biomarker after treatment by AZD4547

Protein modulation by AZD5153 in hematological cell lines

Supplementary figures S1: volcano plot of insensitive cell lines treated by AZD4547 S2:Immunohistochemistry of p-S6 and p-ERK tissue sections from xenograft treated with AZD4547 S3:FGFR pathway modulation in xenograft models treated with AZD4547 S4: ANC3A xenograft tumour growth inhibition after treated with AZD4547 and/or AZD5363 S5: AZD4547 dynamic biomarkers in ANC3A model after 14 days treatment with AKT and FGFR inhibitors

Cell line panel used to identify AZD4547 dynamic transcript biomarkers.

Cell line authentication information

The challenge of developing effective pharmacodynamic biomarkers for preclinical and clinical testing of FGFR signaling inhibition is significant. Assays that rely on the measurement of phospho-protein epitopes can be limited by the availability of effective antibody detection reagents. Transcript profiling enables accurate quantification of many biomarkers and provides a broader representation of pathway modulation. To identify dynamic transcript biomarkers of FGFR signaling inhibition by AZD4547, a potent inhibitor of FGF receptors 1, 2, and 3, a gene expression profiling study was performed in FGFR2-amplified, drug-sensitive tumor cell lines. Consistent with known signaling pathways activated by FGFR, we identified transcript biomarkers downstream of the RAS-MAPK and PI3K/AKT pathways. Using different tumor cell lines in vitro and xenografts in vivo, we confirmed that some of these transcript biomarkers (DUSP6, ETV5, YPEL2) were modulated downstream of oncogenic FGFR1, 2, 3, whereas others showed selective modulation only by FGFR2 signaling (EGR1). These transcripts showed consistent time-dependent modulation, corresponding to the plasma exposure of AZD4547 and inhibition of phosphorylation of the downstream signaling molecules FRS2 or ERK. Combination of FGFR and AKT inhibition in an FGFR2-mutated endometrial cancer xenograft model enhanced modulation of transcript biomarkers from the PI3K/AKT pathway and tumor growth inhibition. These biomarkers were detected on the clinically validated nanoString platform. Taken together, these data identified novel dynamic transcript biomarkers of FGFR inhibition that were validated in a number of in vivo models, and which are more robustly modulated by FGFR inhibition than some conventional downstream signaling protein biomarkers. Mol Cancer Ther; 15(11); 2802–13. ©2016 AACR.

Gene symbol and primer ID used for targeted profiling on Fluidigm arrays

Differential gene expression and statistical analysis of cell lines treated with AZD4547 or DMSO over time

supplementary figures and tables legend

The bromodomain and extraterminal (BET) protein BRD4 regulates gene expression via recruitment of transcriptional regulatory complexes to acetylated chromatin. Pharmacological targeting of BRD4 bromodomains by small molecule inhibitors has proven to be an effective means to disrupt aberrant transcriptional programs critical for tumor growth and/or survival. Herein, we report AZD5153, a potent, selective, and orally available BET/BRD4 bromodomain inhibitor possessing a bivalent binding mode. Unlike previously described monovalent inhibitors, AZD5153 ligates two bromodomains in BRD4 simultaneously. The enhanced avidity afforded through bivalent binding translates into increased cellular and antitumor activity in preclinical hematologic tumor models. In vivo administration of AZD5153 led to tumor stasis or regression in multiple xenograft models of acute myeloid leukemia, multiple myeloma, and diffuse large B-cell lymphoma. The relationship between AZD5153 exposure and efficacy suggests that prolonged BRD4 target coverage is a primary efficacy driver. AZD5153 treatment markedly affects transcriptional programs of MYC, E2F, and mTOR. Of note, mTOR pathway modulation is associated with cell line sensitivity to AZD5153. Transcriptional modulation of MYC and HEXIM1 was confirmed in AZD5153-treated human whole blood, thus supporting their use as clinical pharmacodynamic biomarkers. This study establishes AZD5153 as a highly potent, orally available BET/BRD4 inhibitor and provides a rationale for clinical development in hematologic malignancies. Mol Cancer Ther; 15(11); 2563–74. ©2016 AACR.

Differential gene expression and two statistical analysis of ANC3A xenograft after treatment by AZD4547 and/or AZD5363 compared to vehicle group, and Combination group compared to singled agent.

Statistical analysis of gene modulation between FGFR1, 2 and 3 dysregulated cell lines.

Differentially expressed genes with AZD5153 treatment

Supplemental Fig S1- S6

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62–66. ©2017 AACR.

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

PARP proteins represent a class of post-translational modification enzymes with diverse cellular functions. Targeting PARPs has proven to be efficacious clinically, but exploration of the therapeutic potential of PARP inhibition has been limited to targeting poly(ADP-ribose) generating PARP, including PARP1/2/3 and tankyrases. The cancer-related functions of mono(ADP-ribose) generating PARP, including PARP6, remain largely uncharacterized. Here, we report a novel therapeutic strategy targeting PARP6 using the first reported PARP6 inhibitors. By screening a collection of PARP compounds for their ability to induce mitotic defects, we uncovered a robust correlation between PARP6 inhibition and induction of multipolar spindle (MPS) formation, which was phenocopied by PARP6 knockdown. Treatment with AZ0108, a PARP6 inhibitor with a favorable pharmacokinetic profile, potently induced the MPS phenotype, leading to apoptosis in a subset of breast cancer cells in vitro and antitumor effects in vivo. In addition, Chk1 was identified as a specific substrate of PARP6 and was further confirmed by enzymatic assays and by mass spectrometry. Furthermore, when modification of Chk1 was inhibited with AZ0108 in breast cancer cells, we observed marked upregulation of p-S345 Chk1 accompanied by defects in mitotic signaling. Together, these results establish proof-of-concept antitumor efficacy through PARP6 inhibition and highlight a novel function of PARP6 in maintaining centrosome integrity via direct ADP-ribosylation of Chk1 and modulation of its activity.Significance:These findings describe a new inhibitor of PARP6 and identify a novel function of PARP6 in regulating activation of Chk1 in breast cancer cells.

A figure showing the process of generating and using PDX models.

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 materials

Supplementary Table 7 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Table 1 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Table 3 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Table 5 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Table 6 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Figures S1-S6. Comparison of genetic alterations across multiple populations resistant to AZD9291 and other EGFR TKIs (S1); Treatment of resistant populations with AZD9291 (S2); Detection and Validation of a novel NRAS E63K mutation (S3); Lysates were prepared from parental PC9 and resistant populations analysed for levels of total and phosphorylated ERK, NRAS and KRAS by western blot (S4); The novel NRAS E63K mutation is an activating mutation that when expressed enhances resistance to cell growth inhibition by gefitinib or AZD9291 in EGFRm cell lines (S5); In vitro combination of AZD9291 with selumetinib induces more profound phenotype inhibition (S6).

Supplementary Methods and References. Description of additional methods and procedures used in the study. Also includes Supplementary References.

Supplementary Figures 8-12 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Figure 5 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Table 4 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Figure 1 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Table 2 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Figure Legend. Legends for Supplementary Figures S1-S6.

Supplementary Figure 2 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Resistance to targeted EGFR inhibitors is likely to develop in EGFR-mutant lung cancers. Early identification of innate or acquired resistance mechanisms to these agents is essential to direct development of future therapies. We describe the detection of heterogeneous mechanisms of resistance within populations of EGFR-mutant cells (PC9 and/or NCI-H1975) with acquired resistance to current and newly developed EGFR tyrosine kinase inhibitors, including AZD9291. We report the detection of NRAS mutations, including a novel E63K mutation, and a gain of copy number of WT NRAS or WT KRAS in cell populations resistant to gefitinib, afatinib, WZ4002, or AZD9291. Compared with parental cells, a number of resistant cell populations were more sensitive to inhibition by the MEK inhibitor selumetinib (AZD6244; ARRY-142886) when treated in combination with the originating EGFR inhibitor. In vitro, a combination of AZD9291 with selumetinib prevented emergence of resistance in PC9 cells and delayed resistance in NCI-H1975 cells. In vivo, concomitant dosing of AZD9291 with selumetinib caused regression of AZD9291-resistant tumors in an EGFRm/T790M transgenic model. Our data support the use of a combination of AZD9291 with a MEK inhibitor to delay or prevent resistance to AZD9291 in EGFRm and/or EGFRm/T790M tumors. Furthermore, these findings suggest that NRAS modifications in tumor samples from patients who have progressed on current or EGFR inhibitors in development may support subsequent treatment with a combination of EGFR and MEK inhibition. Cancer Res; 75(12); 2489–500. ©2015 AACR.

Supplementary Figure 4 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary References from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Tables S1-S4. Generation of resistant cell populations (S1); Small molecule inhibitors (S2); IC50 (µM) values from cell growth inhibition assays comparing compound sensitivity between parental and resistant cell populations (S3); Genetic analysis of resistant cell populations (S4).

Supplementary Figure 6 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Figure 7 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Figure 3 from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Supplementary Table and Figure Legends from Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

BackgroundWith the introduction of DNA-damaging therapies into standard of care cancer treatment, there is a growing need for predictive diagnostics assessing homologous recombination deficiency (HRD) status across tumor types. Following the strong clinical evidence for the utility of DNA-sequencing-based HRD testing in ovarian cancer, and growing evidence in breast cancer, we present analytical validation of the Tempus HRD-DNA test. We further developed, validated, and explored the Tempus HRD-RNA model, which uses gene expression data from 16,750 RNA-seq samples to predict HRD status from formalin-fixed paraffin-embedded tumor samples across numerous cancer types.MethodsGenomic and transcriptomic profiling was performed using next-generation sequencing from Tempus xT, Tempus xO, Tempus xE, Tempus RS, and Tempus RS.v2 assays on 48,843 samples. Samples were labeled based on theirBRCA1, BRCA2and selected Homologous Recombination Repair pathway gene (CDK12, PALB2, RAD51B, RAD51C, RAD51D) mutational status to train and validate HRD-DNA, a genome-wide loss-of-heterozygosity biomarker, and HRD-RNA, a logistic regression model trained on gene expression.ResultsIn a sample of 2058 breast and 1216 ovarian tumors, BRCA status was predicted by HRD-DNA with F1-scores of 0.98 and 0.96, respectively. Across an independent set of 1363 samples across solid tumor types, the HRD-RNA model was predictive of BRCA status in prostate, pancreatic, and non-small cell lung cancer, with F1-scores of 0.88, 0.69, and 0.62, respectively.ConclusionsWe predict HRD-positive patients across many cancer types and believe both HRD models may generalize to other mechanisms of HRD outside of BRCA loss. HRD-RNA complements DNA-based HRD detection methods, especially for indications with low prevalence of BRCA alterations.

Purpose:There are several agents in early clinical trials targeting components of the adenosine pathway including A2AR and CD73. The identification of cancers with a significant adenosine drive is critical to understand the potential for these molecules. However, it is challenging to measure tumor adenosine levels at scale, thus novel, clinically tractable biomarkers are needed.Experimental Design:We generated a gene expression signature for the adenosine signaling using regulatory networks derived from the literature and validated this in patients. We applied the signature to large cohorts of disease from The Cancer Genome Atlas (TCGA) and cohorts of immune checkpoint inhibitor–treated patients.Results:The signature captures baseline adenosine levels in vivo (r2 = 0.92, P = 0.018), is reduced after small-molecule inhibition of A2AR in mice (r2 = −0.62, P = 0.001) and humans (reduction in 5 of 7 patients, 70%), and is abrogated after A2AR knockout. Analysis of TCGA confirms a negative association between adenosine and overall survival (OS, HR = 0.6, P < 2.2e–16) as well as progression-free survival (PFS, HR = 0.77, P = 0.0000006). Further, adenosine signaling is associated with reduced OS (HR = 0.47, P < 2.2e–16) and PFS (HR = 0.65, P = 0.0000002) in CD8+ T-cell–infiltrated tumors. Mutation of TGFβ superfamily members is associated with enhanced adenosine signaling and worse OS (HR = 0.43, P < 2.2e–16). Finally, adenosine signaling is associated with reduced efficacy of anti-PD1 therapy in published cohorts (HR = 0.29, P = 0.00012).Conclusions:These data support the adenosine pathway as a mediator of a successful antitumor immune response, demonstrate the prognostic potential of the signature for immunotherapy, and inform patient selection strategies for adenosine pathway modulators currently in development.

Reproducibility of results obtained using ribonucleic acid (RNA) data across labs remains a major hurdle in cancer research. Often, molecular predictors trained on one dataset cannot be applied to another due to differences in RNA library preparation and quantification, which inhibits the validation of predictors across labs. While current RNA correction algorithms reduce these differences, they require simultaneous access to patient-level data from all datasets, which necessitates the sharing of training data for predictors when sharing predictors. Here, we describe SpinAdapt, an unsupervised RNA correction algorithm that enables the transfer of molecular models without requiring access to patient-level data. It computes data corrections only via aggregate statistics of each dataset, thereby maintaining patient data privacy. Despite an inherent trade-off between privacy and performance, SpinAdapt outperforms current correction methods, like Seurat and ComBat, on publicly available cancer studies, including TCGA and ICGC. Furthermore, SpinAdapt can correct new samples, thereby enabling unbiased evaluation on validation cohorts. We expect this novel correction paradigm to enhance research reproducibility and to preserve patient privacy.

Resistance to EGFR inhibitors (EGFRi) presents a major obstacle in treating non-small cell lung cancer (NSCLC). One of the most exciting new ways to find potential resistance markers involves running functional genetic screens, such as CRISPR, followed by manual triage of significantly enriched genes. This triage process to identify ‘high value’ hits resulting from the CRISPR screen involves manual curation that requires specialized knowledge and can take even experts several months to comprehensively complete. To find key drivers of resistance faster we build a recommendation system on top of a heterogeneous biomedical knowledge graph integrating pre-clinical, clinical, and literature evidence. The recommender system ranks genes based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance. This unbiased approach identifies 57 resistance markers from >3,000 genes, reducing hit identification time from months to minutes. In addition to reproducing known resistance markers, our method identifies previously unexplored resistance mechanisms that we prospectively validate.

Third-generation EGFR tyrosine kinase inhibitors (EGFR-TKIs), including osimertinib, an irreversible EGFR-TKI, are important treatments for non-small cell lung cancer with EGFR-TKI sensitizing or EGFR T790M resistance mutations. While patients treated with osimertinib show clinical benefit, disease progression and drug resistance are common. Emergence of de novo acquired resistance from a drug tolerant persister (DTP) cell population is one mechanism proposed to explain progression on osimertinib and other targeted cancer therapies. Here we profiled osimertinib DTPs using RNA-seq and ATAC-seq to characterize the features of these cells and performed drug screens to identify therapeutic vulnerabilities. We identified several vulnerabilities in osimertinib DTPs that were common across models, including sensitivity to MEK, AURKB, BRD4, and TEAD inhibition. We linked several of these vulnerabilities to gene regulatory changes, for example, TEAD vulnerability was consistent with evidence of Hippo pathway turning off in osimertinib DTPs. Last, we used genetic approaches using siRNA knockdown or CRISPR knockout to validate AURKB, BRD4, and TEAD as the direct targets responsible for the vulnerabilities observed in the drug screen.

Background DNA repair deficiencies are characteristic of cancer and homologous recombination deficiency (HRD) is the most common. HRD sensitizes tumour cells to PARP inhibitors so it is important to understand the landscape of HRD across different solid tumour types. Methods Germline and somatic BRCA mutations in breast and ovarian cancers were evaluated using sequencing data from The Cancer Genome Atlas (TCGA) database. Secondly, a larger independent genomic dataset was analysed to validate the TCGA results and determine the frequency of germline and somatic mutations across 15 different candidate homologous recombination repair (HRR) genes, and their relationship with the genetic events of bi-allelic loss, loss of heterozygosity (LOH) and tumour mutation burden (TMB). Results Approximately one-third of breast and ovarian cancer BRCA mutations were somatic. These showed a similar degree of bi-allelic loss and clinical outcomes to germline mutations, identifying potentially 50% more patients that may benefit from precision treatments. HRR mutations were present in sizable proportions in all tumour types analysed and were associated with high TMB and LOH scores. We also identified numerous BRCA reversion mutations across all tumour types. Conclusions Our results will facilitate future research into the efficacy of precision oncology treatments, including PARP and immune checkpoint inhibitors.