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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.

The Cancer Dependency Map Portal enables combined analyses of genomic and phenomic data such as genomic alterations and CRISPR gene dependencies but focuses on single genes or cell lines. This is well suited for verifying previously generated hypotheses such as the dependency of cells on PRMT5 in the setting of loss of function mutations in MTAP. To identify additional actionable de-novo vulnerabilities in-silico, we refined the genomic status of 1000 cell lines by 1. focusing on variants (driver mutations and copy number alterations) that have higher prevalence in a given cancer type in TCGA and 2. reclassifying variants as actionable only if they cause loss of function in known tumour suppressor genes or gain of function in oncogenes. This gives a clear line of sight, a more manageable set of variants and is analogous to Mendelian randomization. This approach was applied to lung squamous cell carcinoma to form a binary event matrix for the shortlisted genes and copy number alterations across a panel of lung squamous cell lines. For each of the variants, we used machine learning approaches to define a dependency signature from CRISPR dependencies for the matching cell lines that distinguish altered vs non-altered cell lines. The top ranking dependencies were further examined for biological relevance and potential toxicity, yielding novel targets to follow up on. The approach generalises well and can be applied to any other cancers with unmet needs. All data in this study was derived from public databases. By combining TCGA cancer specific prevalent genomic alterations in tumor suppressor and oncogenes we drew clear line of sight and associated these alterations to CRISPR based vulnerabilities. This yielded a manageable set of alterations, and for each of these we identified a set of essential genes/pathways to use in machine learning. These signatures were further refined by biological enrichment, targetability and toxicity in order to build a process for rational novel target ID generation. Citation Format: Miika Ahdesmäki, Joshua Armenia, Krishna Bulusu, Ben Sidders, Jonathan Dry, Ultan McDermott. Cancer dependency signatures for line of sight genomic alterations in squamous cell lung cancer yield rational targets [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3379.

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.

This abstract has been withheld from publication due to its inclusion in the AACR Annual Meeting 2018 Official Press Program. It will be posted online following its presentation. Citation Format: Jonathan R. Dry, Michael P. Menden, Krishna Bulusu, Justin Guinney, Julio Saez-Rodriguez. A large cancer pharmacogenomics combination screen powering crowd-sourced advancement of computational drug synergy predictions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3886.

Exciting clinical benefit has been seen from both immune checkpoint inhibitors and PARP inhibitors in several cancer types. Initial reports have suggested the tumor mutational burden (TMB) may be a tumor agnostic marker for predicting immunotherapy (IO) response. Deficiencies in DNA damage repair (DDR) resulting from BRCA1 and BRCA2 mutations can lead to an accumulation of tumor mutations, and sensitivity to PARP inhibitors. We hypothesized that patients with germline or somatic mutations in BRCA1/2 or other homologous recombination repair (HRR) genes, termed “HRRm”, will have higher TMB, higher immune infiltration and activity, and greater benefit from the combination of DDR and IO therapies. We investigated the relationship between DDR deficiencies, TMB, PDL1, transcriptomic immunoscores and drug response across The Cancer Genome Atlas (TCGA) disease cohorts, data from AstraZeneca's clinical studies and a linked NSCLC genomic and EHR dataset from Foundation Medicine and FlatIron Health. HRRm cancer samples show higher TMB in several cancer types. This does trend with higher PDL1 but does not associate with transcriptomic immunoscores. HRRm could be a practical clinical biomarker to select patients with DDR defects likely to benefit from combination of DDR and IO therapies. Citation Format: Jonathan R. Dry, Zhongwu Lai, Brian Dougherty, Carl Barrett, Steven Criscione. Homologous recombination repair gene mutations for DNA repair and immune oncology drug combinations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5724.

PARP inhibitors (PARPi) are promising novel therapies for tumors harboring defects in homologous recombination (HR) DNA repair. Loss of function (LoF) mutations in the tumor suppressors BRCA1 and BRCA2, two critical components in HR pathway, are the most common cause of HR deficiency in tumors. Complete loss of BRCA1/2 function requires bi-allelic loss, and it is hypothesized to be needed for PARPi sensitivity. However, the degrees of bi-allelic loss in breast and ovarian tumors are still lacking consensus, especially when both LoF events are somatic. Recent publications shown a high percentage of germline BRCA2 lacking LOH, including TCGA’s breast and ovarian cohorts. To better understand the loss of wild type BRCA genes, we reanalyzed raw data from TCGA using a novel pipeline to identify both germline and somatic BRCA mutations in breast and ovarian. We then applied mathematical models to determine LOH or bi-allelic loss of those mutations. Our analysis shows a higher degree of bi-allelic loss of BRCA mutations than previously reported, especially germline BRCA2 in breast cancer, which shows nearly 80%. Furthermore, somatic BRCA mutations, which account for about 1/3 of all BRCA mutations, showed a similar degree of bi-allelic loss as germline BRCA mutations. In addition, somatic and germline BRCA were shown to have comparable HRD scores, a measure of tumor genomic instability. Germline BRCA patients are typically diagnosed at younger ages in breast and ovarian cancer, whereas somatic show no difference to non-BRCA patients. In ovarian cancer, patients with somatic or germline BRCA had similar progression-free and overall survival with comparable treatment journeys, suggesting a phenotypic equivalence. Interestingly, BRCA2 tumors showed lower HRD scores than BRCA1 tumors in both breast and ovarian. In breast cancer BRCA1 mutation is significantly enriched in TNBC, but the degree of bi-allelic loss did not show any significant difference between ER+ and TNBC subtypes. To validate our findings, we examined a much larger cohort of breast (n > 5,000) and ovarian (n > 2,500) cancers sequenced by Foundation Medicine, with germline/somatic and LOH status determined computationally using validated methodology. We found similar prevalence of somatic BRCA mutations and the degree of bi-allelic loss and HRD scores in both germline and somatic BRCA mutations. In addition, potential reactivating reversion mutations were discovered in both somatic and germline mutated BRCA in both breast and ovarian cancers, suggesting they are driver mutations. In summary, large scale analysis of BRCA mutations in breast and ovarian cancers suggest that about 1/3 of BRCA mutations are somatic, and both germline and somatic BRCA mutations show high degree of bi-allelic loss and similar genomic instability and may benefit from PARPi therapies. Citation Format: Zhongwu Lai, Darren Hodgson, James Sun, Adrienne Johnson, Carl Barrett, Jonathan Dry. Both somatic and germline BRCA mutations show high degree of bi-allelic loss in breast and ovarian cancers [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 1747.

Personalized therapy is a major goal of modern oncology, as patient responses vary greatly even within a histologically defined cancer subtype. This is especially true in acute myeloid leukemia (AML), which exhibits striking heterogeneity in molecular segmentation. When calibrated to cell-specific data, executable network models can reveal subtle differences in signaling that help explain differences in drug response. Furthermore, they can suggest drug combinations to increase efficacy and combat acquired resistance. Here, we experimentally tested dynamic proteomic changes and phenotypic responses in diverse AML cell lines treated with pan-PIM kinase inhibitor and fms-related tyrosine kinase 3 (FLT3) inhibitor as single agents and in combination. We constructed cell-specific executable models of the signaling axis, connecting genetic aberrations in FLT3, tyrosine kinase 2 (TYK2), platelet-derived growth factor receptor alpha (PDGFRA), and fibroblast growth factor receptor 1 (FGFR1) to cell proliferation and apoptosis via the PIM and PI3K kinases. The models capture key differences in signaling that later enabled them to accurately predict the unique proteomic changes and phenotypic responses of each cell line. Furthermore, using cell-specific models, we tailored combination therapies to individual cell lines and successfully validated their efficacy experimentally. Specifically, we showed that cells mildly responsive to PIM inhibition exhibited increased sensitivity in combination with PIK3CA inhibition. We also used the model to infer the origin of PIM resistance engineered through prolonged drug treatment of MOLM16 cell lines and successfully validated experimentally our prediction that this resistance can be overcome with AKT1/2 inhibition. Cancer Res; 77(4); 827–38. ©2016 AACR.

Selumetinib (AZD6244, ARRY-142886) is a selective, non–ATP-competitive inhibitor of mitogen-activated protein/extracellular signal–regulated kinase kinase (MEK)-1/2. The range of antitumor activity seen preclinically and in patients highlights the importance of identifying determinants of response to this drug. In large tumor cell panels of diverse lineage, we show that MEK inhibitor response does not have an absolute correlation with mutational or phospho-protein markers of BRAF/MEK, RAS, or phosphoinositide 3-kinase (PI3K) activity. We aimed to enhance predictivity by measuring pathway output through coregulated gene networks displaying differential mRNA expression exclusive to resistant cell subsets and correlated to mutational or dynamic pathway activity. We discovered an 18-gene signature enabling measurement of MEK functional output independent of tumor genotype. Where the MEK pathway is activated but the cells remain resistant to selumetinib, we identified a 13-gene signature that implicates the existence of compensatory signaling from RAS effectors other than PI3K. The ability of these signatures to stratify samples according to functional activation of MEK and/or selumetinib sensitivity was shown in multiple independent melanoma, colon, breast, and lung tumor cell lines and in xenograft models. Furthermore, we were able to measure these signatures in fixed archival melanoma tumor samples using a single RT-qPCR–based test and found intergene correlations and associations with genetic markers of pathway activity to be preserved. These signatures offer useful tools for the study of MEK biology and clinical application of MEK inhibitors, and the novel approaches taken may benefit other targeted therapies. Cancer Res; 70(6); 2264–73

Detection of somatic variants in tumor tissues is far from a solved problem. Heterogenous tumor materials complicate variant assessment in low frequency subclonal populations, which can play an important role in the development of drug resistance. Loss of heterozygosity, copy number variation and other ploidy changes can further change the expected distribution of variant metrics. We demonstrate improvements in the specificity and sensitivity of variant calling algorithms by optimising linear combinations of widely used filtering criteria such as allelic frequency, depth and p-value distributions. We specifically improve SNP and indels from the freely available VarDict caller (https://github.com/AstraZeneca-NGS/VarDictJava) and show that these optimisations can be generalised across a wide range of allelic frequencies. We validate our findings against synthetic and in vitro standards such as the ICGC-TCGA DREAM challenge truth set. Our proposed filtering strategy is widely applicable to other variant callers, and the updated filters for VarDict dramatically improve sensitivity and precision on low frequency variants which are crucial for our ability to reconstruct likely tumor populations from short read sequencing data and to study tumor evolution. Citation Format: Zhongwu Lai, Brad Chapman, Miika Ahdesmäki, Oliver Hofmann, Justin Johnson, Jonathan Dry. Optimizing the detection of subclonal somatic variants with VarDict. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 166.

Targeted sequencing is used increasingly in clinics to guide therapeutic decisions by measuring mutations, small indels and/or rearrangements in cancer genes. An ability to use the same platform to detect additional oncogene activation (or tumor suppressor loss) through copy number changes could significantly expand the number of patients able to benefit from targeted therapies. Many currently available tools either requires a matched normal, works only for whole genome or exome sequencing, or don't work for PCR-based targeted sequencing. In addition, no tools available to detect breakpoints within a gene from targeted sequencing. A versatile copy number analysis tool from sequencing is needed to maximize the value of the sequencing routinely applied in clinics. Here we presented a novel computational tool, Seq2C (Sequencing To Copy Number), which is versatile to handle various situations and reports aberrations at gene level ready for interpretation. Seq2C works at cohort level and does not require a matched ‘normal’, though it can optionally use one or more normal samples for small ( Another distinct feature differentiating Seq2C from other currently available tools is that Seq2C identifies breakpoints and detects one or more exon deletion or duplication rearrangements within a gene, which is common in tumor suppressors as a mechanism to lose function. In addition, it can also detect potential fusions in genes such as ALK and ERG, where a copy number change is often accompanied with the fusion and a breakpoint can thus be called by Seq2C. Furthermore, it can predict gender from exome or whole genome sequencing. We applied Seq2C to exome sequencing of CCLE cell lines, and showed that it produced gene level copy number data highly correlated to those derived from microarrays, the current gold standard. Interestingly, Seq2C identified one or more exon deletions in several common tumor suppressors, such as TP53, PTEN, CDKN2A, NF1, STK11 and RB1, in cell lines with no known aberrations. They were supported by RNA-Seq data. It also correctly called known ERG fusion in prostate cell line NCI-H660 and identified a previously un-reported EML4-ALK fusion in a pancreatic cancer cell line SNU-324, which is confirmed by RNA-Seq data, suggesting EML4-ALK is not limited to lung cancer. In conclusion, Seq2C is a versatile copy number analysis tool for sequencing and will be useful for cancer research. Seq2C is freely available in GitHub. Citation Format: Zhongwu Lai, Aleksandra Markovets, Jonathan Dry. Seq2C: from sequence to copy number for cancer samples. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5268.

Our current understanding of the mutation spectrum of relapse/refractory patients is limited. Public sequencing efforts have focused by design, on primary tumors, while re-biopsy of patients who are refractory to first line therapy or who relapse on treatment is not standard protocol. We have performed whole exome sequencing on post standard of care biopsies from 3 patient cohorts, 47 DLBCL patients post-relapse following R-CHOP, 49 triple negative breast (TNBC) following taxol alone or in combination and 41 lung adenocarcinoma (LUAD) following platinum alone or in combination. Comparative analysis of the mutation spectrum of these cohorts was performed against available clinically matched primary tumor cohorts. Comparison of the mutational signatures, somatic variant frequencies and copy number variation between matched pre- and post-treatment samples, and treated-primary tumor cohorts were performed. Significant differences were identified in the frequency of oncogenic drivers including PIK3CA in TNBC and KRAS in LUAD. We also observed mutational signature differences between cohorts that could impact treatment regimens post standard of care. Collectively these data further highlight the value of re-assessing the tumor genetic landscape at the time of treatment rather than relying on primary tumor samples, which may not be representative of the cohort to be treated. Citation Format: Danielle Greenawalt, Austin Dulak, Manasa Ramakrishna, Zhongwu Lai, Justin Johnson, Hongyue Dai, Melissa Mitchell, Carl Barrett, Stephen Fawell, Jonathan Dry. Your targeted population might not be what you predict: Changes in tumor genetic landscapes post standard of care. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3168.

AZD4547 is an orally bio-available, highly selective and potent small molecule inhibitor, ATP competitive inhibitor of FGF receptors 1, 2 and 3. To identify novel dynamic transcript biomarkers of FGFR signalling inhibition by AZD4547, a gene expression profiling study was performed. Cell lines defined as sensitive or resistant to AZD4547 (n=10 for each class) were treated with AZD4547 or DMSO control for 2, 6 or 24 hours and processed for microarray analysis. Amongst those genes modulated by AZD4547, the MEK signature genes DUSP6 and ETV5, together with the MAPK pathway regulator SPRED1 were repressed on AZD4547 treatment across all sensitive cell lines over time, consistent with FGFR signalling through this pathway. Further in vivo validation using three FGFR sensitive/responsive xenograft models (SNU16, KMS11, KG1a) was performed. We observed time dependent repression (16h to 24h) of DUSP6, ETV5 and SPRED1 across all three xenograft models which was consistent with plasma exposure of AZD4547 in vivo. In addition, modulation of these transcript biomarkers corresponded with inhibition of phosphorylation of FGFR, FRS2 and ERK, supporting engagement of the FGFR signalling pathway. Taken together, these data identify novel dynamic biomarkers of FGFR inhibition in vivo. Citation Format: Oona Delpuech, Claire Rooney, Lorraine Mooney, Robert McEwen, Sarah Fenton, Dawn Baker, Robert Shaw, Jonathan Dry, Elaine Kilgour, Paul Smith. Identification of novel dynamic biomarkers of FGFR inhibition by the FGFR1,2 and 3 selective inhibitor AZD4547. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 908. doi:10.1158/1538-7445.AM2013-908

Background: Early results from Phase II trials have shown that treatment with the PARP inhibitor olaparib (AZD2281, KU-0059436) can induce tumour specific synthetic lethality in patients with BRCA-mutated breast and ovarian cancer, with little effect on normal tissues. Pre-clinical studies have indicated that sensitivity can also result from other perturbations affecting homologous recombination (HR), suggesting broader patient populations may benefit from this drug. We aimed to generate pre-clinical hypotheses as to the key biological mechanisms regulating response to this drug; and identify associated biomarkers by which responding patient subsets could be stratified. Methods: A panel of 95 cell lines representing breast, ovarian, colorectal, lung, head & neck and pancreatic cancers was tested for sensitivity to olaparib using 2D-clonogenic survival assays. Baseline (untreated) gene expression profiles (Affymetrix U133A Plus2) were determined for each cell line, alongside protein expression and mutational status of core HR genes. Dynamic pathway gene expression signatures were collated from the literature, and additional signatures generated using RNAi against core HR components. Statistical and bioinformatic approaches were then applied to prioritise correlated networks of genes displaying consistent pathway overlay and predictive of response in cell line subsets. Results: 30 cell lines were highly sensitive to olaparib treatment (IC50 4 µM). Deleterious mutations in BRCA1 or BRCA2 genes were associated with only a small subset of highly sensitive cell lines, highlighting the presence of other factors able to modulate olaparib responsiveness. Transcriptome analysis revealed DNA repair and proliferation associated genes to be most consistently correlated with olaparib response. Based on these results, a candidate predictive baseline gene expression profile was established. Conclusions: By profiling a large panel of cell lines we have determined that factors in addition to BRCA mutation can be linked with olaparib sensitivity. A list of candidate gene transcripts predictive of in vitro sensitivity to olaparib were identified which may have utility as predictive biomarkers in the clinic. Although primarily linked to mechanisms expected to influence response (DNA repair, cell-cycle checkpoints and proliferation), novel roles were suggested for other pathways such as Aurora A kinase signalling. Analyses also highlighted that some classical HR pathway components may prove to be more robust mRNA markers of pathway activity than others. Studies are already underway to determine whether this baseline transcript tumour profile can be correlated with patient responses to olaparib. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3497.