The JAX Cancer Center

Microglia are essential for maintenance of normal brain function, with dysregulation contributing to numerous neurological diseases. Protocols have been developed to derive microglia-like cells from human induced pluripotent stem cells (hiPSCs). However, primary microglia display major differences in morphology and gene expression when grown in culture, including down-regulation of signature microglial genes. Thus, in vitro differentiated microglia may not accurately represent resting primary microglia. To address this issue, we transplanted microglial precursors derived in vitro from hiPSCs into neonatal mouse brains and found that the cells acquired characteristic microglial morphology and gene expression signatures that closely resembled primary human microglia. Single-cell RNA-sequencing analysis of transplanted microglia showed similar cellular heterogeneity as primary human cells. Thus, hiPSCs-derived microglia transplanted into the neonatal mouse brain assume a phenotype and gene expression signature resembling that of resting microglia residing in the human brain, making chimeras a superior tool to study microglia in human disease.

BACKGROUND: Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site of origin cannot be found despite a standard diagnostic workup. Because knowledge of a patient's primary cancer remains fundamental to their treatment, CUP patients are significantly disadvantaged and most have a poor survival outcome. Developing robust and accessible diagnostic methods for resolving cancer tissue of origin, therefore, has significant value for CUP patients. METHODS: We developed an RNA-based classifier called CUP-AI-Dx that utilizes a 1D Inception convolutional neural network (1D-Inception) model to infer a tumor's primary tissue of origin. CUP-AI-Dx was trained using the transcriptional profiles of 18,217 primary tumours representing 32 cancer types from The Cancer Genome Atlas project (TCGA) and International Cancer Genome Consortium (ICGC). Gene expression data was ordered by gene chromosomal coordinates as input to the 1D-CNN model, and the model utilizes multiple convolutional kernels with different configurations simultaneously to improve generality. The model was optimized through extensive hyperparameter tuning, including different max-pooling layers and dropout settings. For 11 tumour types, we also developed a random forest model that can classify the tumour's molecular subtype according to prior TCGA studies. The optimised CUP-AI-Dx tissue of origin classifier was tested on 394 metastatic samples from 11 tumour types from TCGA and 92 formalin-fixed paraffin-embedded (FFPE) samples representing 18 cancer types from two clinical laboratories. The CUP-AI-Dx molecular subtype was also independently tested on independent ovarian and breast cancer microarray datasets FINDINGS: CUP-AI-Dx identifies the primary site with an overall top-1-accuracy of 98.54% in cross-validation and 96.70% on a test dataset. When applied to two independent clinical-grade RNA-seq datasets generated from two different institutes from the US and Australia, our model predicted the primary site with a top-1-accuracy of 86.96% and 72.46% respectively. INTERPRETATION: The CUP-AI-Dx predicts tumour primary site and molecular subtype with high accuracy and therefore can be used to assist the diagnostic work-up of cancers of unknown primary or uncertain origin using a common and accessible genomics platform. FUNDING: NIH R35 GM133562, NCI P30 CA034196, Victorian Cancer Agency Australia.

Alternative splicing allows expression of mRNA isoforms from a single gene, expanding the diversity of the proteome. Its prevalence in normal biological and disease processes warrant precise tools for modulation. Here we report the engineering of CRISPR Artificial Splicing Factors (CASFx) based on RNA-targeting CRISPR-Cas systems. We show that simultaneous exon inclusion and exclusion can be induced at distinct targets by differential positioning of CASFx. We also create inducible CASFx (iCASFx) using the FKBP-FRB chemical-inducible dimerization domain, allowing small molecule control of alternative splicing. Finally, we demonstrate the activation of SMN2 exon 7 splicing in spinal muscular atrophy (SMA) patient fibroblasts, suggesting a potential application of the CASFx system.

Tumor immunology has changed the landscape of cancer treatment. Yet, not all patients benefit as cancer immune responsiveness (CIR) remains a limitation in a considerable proportion of cases. The multifactorial determinants of CIR include the genetic makeup of the patient, the genomic instability central to cancer development, the evolutionary emergence of cancer phenotypes under the influence of immune editing, and external modifiers such as demographics, environment, treatment potency, co-morbidities and cancer-independent alterations including immune homeostasis and polymorphisms in the major and minor histocompatibility molecules, cytokines, and chemokines. Based on the premise that cancer is fundamentally a disorder of the genes arising within a cell biologic process, whose deviations from normality determine the rules of engagement with the host's response, the Society for Immunotherapy of Cancer (SITC) convened a task force of experts from various disciplines including, immunology, oncology, biophysics, structural biology, molecular and cellular biology, genetics, and bioinformatics to address the complexity of CIR from a holistic view. The task force was launched by a workshop held in San Francisco on May 14-15, 2018 aimed at two preeminent goals: 1) to identify the fundamental questions related to CIR and 2) to create an interactive community of experts that could guide scientific and research priorities by forming a logical progression supported by multiple perspectives to uncover mechanisms of CIR. This workshop was a first step toward a second meeting where the focus would be to address the actionability of some of the questions identified by working groups. In this event, five working groups aimed at defining a path to test hypotheses according to their relevance to human cancer and identifying experimental models closest to human biology, which include: 1) Germline-Genetic, 2) Somatic-Genetic and 3) Genomic-Transcriptional contributions to CIR, 4) Determinant(s) of Immunogenic Cell Death that modulate CIR, and 5) Experimental Models that best represent CIR and its conversion to an immune responsive state. This manuscript summarizes the contributions from each group and should be considered as a first milestone in the path toward a more contemporary understanding of CIR. We appreciate that this effort is far from comprehensive and that other relevant aspects related to CIR such as the microbiome, the individual's recombined T cell and B cell receptors, and the metabolic status of cancer and immune cells were not fully included. These and other important factors will be included in future activities of the taskforce. The taskforce will focus on prioritization and specific actionable approach to answer the identified questions and implementing the collaborations in the follow-up workshop, which will be held in Houston on September 4-5, 2019.

Cancer cells adapt their metabolic activities to support growth and proliferation. However, increased activity of metabolic enzymes is not usually considered an initiating event in the malignant process. Here, we investigate the possible role of the enzyme serine hydroxymethyltransferase-2 (SHMT2) in lymphoma initiation. SHMT2 localizes to the most frequent region of copy number gains at chromosome 12q14.1 in lymphoma. Elevated expression of SHMT2 cooperates with BCL2 in lymphoma development; loss or inhibition of SHMT2 impairs lymphoma cell survival. SHMT2 catalyzes the conversion of serine to glycine and produces an activated one-carbon unit that can be used to support S-adenosyl methionine synthesis. SHMT2 induces changes in DNA and histone methylation patterns leading to promoter silencing of previously uncharacterized mutational genes, such as SASH1 and PTPRM. Together, our findings reveal that amplification of SHMT2 in cooperation with BCL2 is sufficient in the initiation of lymphomagenesis through epigenetic tumor suppressor silencing. Parsa et al. report a mechanism of lymphoma initiation involving cooperation of BCL2 and increased activity of the metabolic enzyme SHMT2, which imparts changes in DNA and histone methylation.

Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biology. Here, we present Sub-Compartment Identifier (SCI), an algorithm that uses graph embedding followed by unsupervised learning to predict sub-compartments using Hi-C chromatin interaction data. We find that the network topological centrality and clustering performance of SCI sub-compartment predictions are superior to those of hidden Markov model (HMM) sub-compartment predictions. Moreover, using orthogonal Chromatin Interaction Analysis by in-situ Paired-End Tag Sequencing (ChIA-PET) data, we confirmed that SCI sub-compartment prediction outperforms HMM. We show that SCI-predicted sub-compartments have distinct epigenetic marks, transcriptional activities, and transcription factor enrichment. Moreover, we present a deep neural network to predict sub-compartments using epigenome, replication timing, and sequence data. Our neural network predicts more accurate sub-compartment predictions when SCI-determined sub-compartments are used as labels for training.

The TET2 DNA hydroxymethyltransferase is frequently disrupted by somatic mutations in diffuse large B cell lymphomas (DLBCLs), a tumor that originates from germinal center (GC) B cells. Here, we show that TET2 deficiency leads to DNA hypermethylation of regulatory elements in GC B cells, associated with silencing of the respective genes. This hypermethylation affects the binding of transcription factors including those involved in exit from the GC reaction and involves pathways such as B cell receptor, antigen presentation, CD40, and others. Normal GC B cells manifest a typical hypomethylation signature, which is caused by AID, the enzyme that mediates somatic hypermutation. However, AID-induced demethylation is markedly impaired in TET2-deficient GC B cells, suggesting that AID epigenetic effects are partially dependent on TET2. Last, we find that TET2 mutant DLBCLs also manifest the aberrant TET2-deficient GC DNA methylation signature, suggesting that this epigenetic pattern is maintained during and contributes to lymphomagenesis.

ChIA-PET (chromatin interaction analysis with paired-end tags) enables genome-wide discovery of chromatin interactions involving specific protein factors, with base pair resolution. Interpretation of ChIA-PET data requires a robust analytic pipeline. Here, we introduce ChIA-PIPE, a fully automated pipeline for ChIA-PET data processing, quality assessment, visualization, and analysis. ChIA-PIPE performs linker filtering, read mapping, peak calling, and loop calling and automates quality control assessment for each dataset. To enable visualization, ChIA-PIPE generates input files for two-dimensional contact map viewing with Juicebox and HiGlass and provides a new dockerized visualization tool for high-resolution, browser-based exploration of peaks and loops. To enable structural interpretation, ChIA-PIPE calls chromatin contact domains, resolves allele-specific peaks and loops, and annotates enhancer-promoter loops. ChIA-PIPE also supports the analysis of other related chromatin-mapping data types.

Supratentorial ependymoma (ST-EPN) is a type of malignant brain tumor mainly seen in children. Since 2014, it has been known that an intrachromosomal fusion C11orf95-RELA is an oncogenic driver in ST-EPN [Parker et al. Nature 506:451-455 (2014); Pietsch et al. Acta Neuropathol 127:609-611 (2014)] but the molecular mechanisms of oncogenesis are unclear. Here we show that the C11orf95 component of the fusion protein dictates DNA binding activity while the RELA component is required for driving the expression of ependymoma-associated genes. Epigenomic characterizations using ChIP-seq and HiChIP approaches reveal that C11orf95-RELA modulates chromatin states and mediates chromatin interactions, leading to transcriptional reprogramming in ependymoma cells. Our findings provide important characterization of the molecular underpinning of C11orf95-RELA fusion and shed light on potential therapeutic targets for C11orf95-RELA subtype ependymoma.

Abstract Background Nanopore long-read sequencing technology greatly expands the capacity of long-range single-molecule DNA-modification detection. A growing number of analytical tools have been actively developed to detect DNA methylation from Nanopore sequencing reads. Here, we examine the performance of different methylation calling tools to provide a systematic evaluation to guide practitioners for human epigenome-wide research. Results We compare five analytic frameworks for detecting DNA modification from Nanopore long-read sequencing data. We evaluate the association between genomic context, CpG methylation-detection accuracy, CpG sites coverage, and running time using Nanopore sequencing data from natural human DNA. Furthermore, we provide an online DNA methylation database ( https://nanome.jax.org ) with which to display genomic regions that exhibit differences in DNA-modification detection power among different methylation calling algorithms for nanopore sequencing data. Conclusions Our study is the first benchmark of computational methods for mammalian whole genome DNA-modification detection in Nanopore sequencing. We provide a broad foundation for cross-platform standardization, and an evaluation of analytical tools designed for genome-scale modified-base detection using Nanopore sequencing.

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma affecting children and is often diagnosed with concurrent metastases. Unfortunately, few effective therapies have been discovered that improve the long-term survival rate for children with metastatic disease. Here we determined effectiveness of targeting the receptor tyrosine kinase, EphB4, in both alveolar and embryonal RMS either directly through the inhibitory antibody, VasG3, or indirectly by blocking both forward and reverse signaling of EphB4 binding to EphrinB2, cognate ligand of EphB4. Clinically, EphB4 expression in eRMS was correlated with longer survival. Experimentally, inhibition of EphB4 with VasG3 in both aRMS and eRMS orthotopic xenograft and allograft models failed to alter tumor progression. Inhibition of EphB4 forward signaling using soluble EphB4 protein fused with murine serum albumin failed to affect eRMS model tumor progression, but did moderately slow progression in murine aRMS. We conclude that inhibition of EphB4 signaling with these agents is not a viable monotherapy for rhabdomyosarcoma.

Abstract The common bacterial base modification N6-methyladenine (m 6 A) is involved in many pathways related to an organism’s ability to survive and interact with its environment. Recent research has shown that nanopore sequencing can detect m 5 C with per-read accuracy of upwards of 80% but m 6 A with significantly lower accuracy. Here we use a binary classifier to improve m 6 A classification by marking adenines as methylated or unmethylated based on differences between measured and expected current values as each adenine travels through the nanopore. We also illustrate the importance of read quality for base modification detection and compare to PacBio methylation calls. With recent demonstrations of nanopore sequencing in Antarctica and onboard the International Space Station, the ability to reliably characterize m 6 A presents an opportunity to further examine the role of methylation in bacterial adaptation to extreme or very remote environments.

Intracellular cytokine staining (ICS) is a widely employed ex vivo method for quantitative determination of the activation status of immune cells, most often applied to T cells. ICS test samples are commonly prepared from animal or human tissues as unpurified cell mixtures, and cell-specific cytokine signals are subsequently discriminated by gating strategies using flow cytometry. Here, we show that when ICS samples contain Ly6G+ neutrophils, neutrophils are ex vivo activated by an ICS reagent – phorbol myristate acetate (PMA) – which leads to hydrogen peroxide (H2O2) release and death of cytokine-expressing T cells. This artifact is likely to result in overinterpretation of the degree of T cell suppression, misleading immunological research related to cancer, infection, and inflammation. We accordingly devised easily implementable improvements to the ICS method and propose alternative methods for assessing or confirming cellular cytokine expression.

Intra-tumoral epigenetic heterogeneity is an indicator of tumor population fitness and is linked to the deregulation of transcription. However, there is no published computational tool to automate the measurement of intra-tumoral epigenetic allelic heterogeneity. We developed an R/Bioconductor package, epihet, to calculate the intra-tumoral epigenetic heterogeneity and to perform differential epigenetic heterogeneity analysis. Furthermore, epihet can implement a biological network analysis workflow for transforming cancer-specific differential epigenetic heterogeneity loci into cancer-related biological function and clinical biomarkers. Finally, we demonstrated epihet utility on acute myeloid leukemia. We found statistically significant differential epigenetic heterogeneity (DEH) loci compared to normal controls and constructed co-epigenetic heterogeneity network and modules. epihet is available at https://bioconductor.org/packages/release/bioc/html/epihet.html .

Heterozygous ARID1B variants result in Coffin-Siris syndrome. Features may include hypoplastic nails, slow growth, characteristic facial features, hypotonia, hypertrichosis, and sparse scalp hair. Most reported cases are due to ARID1B loss of function variants. We report a boy with developmental delay, feeding difficulties, aspiration, recurrent respiratory infections, slow growth, and hypotonia without a clinical diagnosis, where a previously unreported ARID1B missense variant was classified as a variant of uncertain significance. The pathogenicity of this variant was refined through combined methodologies including genome-wide methylation signature analysis (EpiSign), Machine Learning (ML) facial phenotyping, and LIRICAL. Trio exome sequencing and EpiSign were performed. ML facial phenotyping compared facial images using FaceMatch and GestaltMatcher to syndrome-specific libraries to prioritize the trio exome bioinformatic pipeline gene list output. Phenotype-driven variant prioritization was performed with LIRICAL. A de novo heterozygous missense variant, ARID1B p.(Tyr1268His), was reported as a variant of uncertain significance. The ACMG classification was refined to likely pathogenic by a supportive methylation signature, ML facial phenotyping, and prioritization through LIRICAL. The ARID1B genotype-phenotype has been expanded through an extended analysis of missense variation through genome-wide methylation signatures, ML facial phenotyping, and likelihood-ratio gene prioritization.

RNA processing and metabolism are subjected to precise regulation in the cell to ensure integrity and functions of RNA. Though targeted RNA engineering has become feasible with the discovery and engineering of the CRISPR-Cas13 system, simultaneous modulation of different RNA processing steps remains unavailable. In addition, off-target events resulting from effectors fused with dCas13 limit its application. Here we developed a novel platform, Combinatorial RNA Engineering via Scaffold Tagged gRNA (CREST), which can simultaneously execute multiple RNA modulation functions on different RNA targets. In CREST, RNA scaffolds are appended to the 3' end of Cas13 gRNA and their cognate RNA binding proteins are fused with enzymatic domains for manipulation. Taking RNA alternative splicing, A-to-G and C-to-U base editing as examples, we developed bifunctional and tri-functional CREST systems for simultaneously RNA manipulation. Furthermore, by fusing two split fragments of the deaminase domain of ADAR2 to dCas13 and/or PUFc respectively, we reconstituted its enzyme activity at target sites. This split design can reduce nearly 99% of off-target events otherwise induced by a full-length effector. The flexibility of the CREST framework will enrich the transcriptome engineering toolbox for the study of RNA biology.

Deep learning algorithms can extract meaningful diagnostic features from biomedical images, promising improved patient care in digital pathology. Vision Transformer (ViT) models capture long-range spatial relationships and offer robust prediction power and better interpretability for image classification tasks than convolutional neural network models. However, limited annotated biomedical imaging datasets can cause ViT models to overfit, leading to false predictions due to random noise. To address this, we introduce Training Attention and Validation Attention Consistency (TAVAC), a metric for evaluating ViT model overfitting and quantifying interpretation reproducibility. By comparing high-attention regions between training and testing, we tested TAVAC on four public image classification datasets and two independent breast cancer histological image datasets. Overfitted models showed significantly lower TAVAC scores. TAVAC also distinguishes off-target from on-target attentions and measures interpretation generalization at a fine-grained cellular level. Beyond diagnostics, TAVAC enhances interpretative reproducibility in basic research, revealing critical spatial patterns and cellular structures of biomedical and other general nonbiomedical images.

Abstract Three-dimensional (3D) structures of the genome are dynamic, heterogeneous and functionally important. Live cell imaging has become the leading method for chromatin dynamics tracking. However, existing CRISPR- and TALE-based genomic labeling techniques have been hampered by laborious protocols and are ineffective in labeling non-repetitive sequences. Here, we report a versatile CRISPR/Casilio-based imaging method that allows for a nonrepetitive genomic locus to be labeled using one guide RNA. We construct Casilio dual-color probes to visualize the dynamic interactions of DNA elements in single live cells in the presence or absence of the cohesin subunit RAD21. Using a three-color palette, we track the dynamic 3D locations of multiple reference points along a chromatin loop. Casilio imaging reveals intercellular heterogeneity and interallelic asynchrony in chromatin interaction dynamics, underscoring the importance of studying genome structures in 4D.

Following publication of the original article [1], the author reported that an author name, Roberta Zappasodi, was missed in the authorship list. Davide Bedognetti, Rongze Lu, Josue Samayoa, Stefani Spranger and Sarah Warren contributed equally to this work. The original article can be found online at 10.1186/s40425-019-0602-4

The term “tailored therapy” has entered the lexicon of terms that we employ to describe stratified or personalized medicine approaches, whether it be in cancer or in other human diseases. Borrowing another phrase from the clothing industry, the idea that we can craft a “bespoke” therapy, underpinned by our knowledge of disease biology, conjures up a similar metaphor that reflects our ability to move away from the traditional “one size fits all” approach that has been the standard of care over the last 30 years. A crucial component of this bespoke therapy is the increasingly important role that genetic context plays in informing the clinical decision making process. Initial successes were predicated on our ability to dissect out the key molecular instigators of hematological malignancies, with chronic myeloid leukemia (CML) representing an early paradigm for disease biology informing clinical application. The evolution of imatinib mesylate, a tyrosine kinase inhibitor (TKI), as a therapy for CML is, in essence, a history lesson in personalized medicine. The observation of the Philadelphia translocation by Nowell and Hungerford, the identification of the Abelson oncogene (ABL) as the key molecular partner by Janet Rowley, the demonstration by George Daley that BCR‐ABL can cause leukemia in mice: these and many other key observations led Brian Druker, Nicholas Lydon, and Charles Sawyer to realize that employing a strategic approach to disrupt the key molecular function of BCR‐ABL, its tyrosine kinase activity, could underpin a therapeutic intervention that is now the standard of care in this malignancy [1]. Recognizing their seminal achievement, Drucker, Lydon, and Sawyer were joint recipients of the Lasker Prize in 2009. Increasingly, the principles that were brought into clinical practice in hematological malignancies are now being applied with varying levels of success in solid tumors [2–4]. However, while the acquired genetic lesion in CML is present in essentially 100% of cases, the molecular landscape in solid tumors is more complex. Initial excitement on the ability of approaches that target the epidermal growth factor receptor (EGFR) to achieve therapeutic efficacy in diseases such as lung or colon cancer was tempered by the realization that genetic context played a crucial role in determining this efficacy. Thus, in non‐small cell lung cancer (NSCLC), the use of EGFR TKIs such as gefitinib or erlotinib is only effective in patients harboring EGFR‐activating mutations. Initial studies indicated that these mutations were only present in 7%–10% of patients [5]. However, these figures were compiled from white patients. In Asian patients, for example, the incidence of EGFR mutations is more than 30%, reflected in a concomitantly higher response rate to anti‐EGFR approaches [6]. Therefore, genetic context should not only be considered at the individual level, but also at the level of the population. It is, thus, at the population level where we must focus more of our efforts. There is a conceptual difference in how we best utilize the promise of discovery biology and personalized medicine when compared with our more traditional approach. Rather than focusing on the diversity of the disease, the emphasis needs to shift to human genomic diversity and the concept of genetic context described above. The example of RAS mutational status predicting response to EGFR inhibitors in colorectal cancer [7] is increasingly being reflected in other malignancies, emphasizing our need to link new therapeutic innovation with diagnostic innovation and the provision (and reimbursement) of companion diagnostics. Returning to CML, a recent study from Asia has indicated a new mechanism of resistance to imatinib mesylate and other TKI inhibitors. Researchers in Singapore used a parallel sequencing approach to identify an intronic 2903 base pair deletion polymorphism in the Bim gene in CML patients who are resistant to TKIs [8]. Bim is a member of the bcl‐2 gene family which regulates cell death. The deletion event leads to a fusion of exons 3 and 4 of the Bim gene, leading to a mutant protein that is unable to regulate cell death. Given that TKIs mediate their effects in CML through cell death pathways, this mutation underpins a new mechanism of resistance to TKI therapy. Evaluating the frequency of this mutation in over 2,500 normal individuals, the researchers revealed that this aberration is present in over 12% of individuals of East Asian origin, but is entirely absent from European or African populations. Intriguingly, this molecular aberration is not confined to mediating resistance to TKI therapy in CML. The researchers also evaluated patients with lung cancer for this deletion and demonstrated both its presence and its role in mediating resistance to erlotinib in the lung cancer patient cohort [9], further emphasizing our need to focus on genetic, rather than disease diversity as we strive to realize the promise of personalized medicine. We need to develop a new roadmap that allows us to exploit our increased understanding of the malignant process, because the challenge lies not so much in defining disease biology, but in translating this biology at both the individual and population levels. However, the potential of personalized cancer medicine is being tempered by a realization that many challenges remain, not least in the potential increased costs associated with targeted therapeutic approaches [10]. Importantly, we need to refine our current algorithm at all stages of the cancer clinical trial continuum, from drug discovery science through phase I development to clinical application. In Europe, the regulatory environment must also evolve, to ensure more effective clinical trial activity for the European cancer patient. The new Clinical Trials Directive [11] and proposals for new data protection [12] and biobanking [13] regulations need to be aligned with the realities of 21st century medicine; otherwise they will significantly limit cancer clinical trial and translational research activity in Europe. Another crucial component in our personalized medicine roadmap is the need for new models of collaboration and cooperation. Academia can deliver on discovery science, not only in the laboratory, but also increasingly in the patient. The mantra of “bench to bedside and back again” is the cornerstone of modern translational medicine. However, the ability to deliver new drugs and therapies is a skill set, that in the main, even large academic institutions do not possess. Industry may be at a crossroads, particularly in the context of the patent cliff, but it still possesses the skills, expertise, and capacity to cross the rubicon and deliver a new class of effective medicines with their origins in excellent discovery science. The crucial requirement is to reposition the academia‐industry intersect, such that our efforts are discovery science led, with all partners performing the tasks that they do best. This will be achieved through a level of innovation, expertise, and added‐value collaboration in a timely fashion that has maximum impact for the most important component of the partnership, the cancer patient. Patrick G. Johnston: AstraZeneca, Chugai Pharmaceuticals, Pfizer, Sanofi‐Aventis, Roche (C/A); AstraZeneca, Amgen, Almac Diagnostics, Fusion Antibodies (RF); Almac Diagnostics (E); Almac Diagnostics Almac, Roche, Chugai Pharmaceuticals, Sanofi‐Aventis (H). The other author indicated no financial relationships.