Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.

We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.

Adaptation of a mutual exclusivity framework to identify driver mutations within oncogenic pathways.

Distinguishing genomic alterations in cancer-associated genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage have important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering functional candidates beyond the existing knowledge base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer-associated gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. We use simulations to study the operating characteristics of the method and assess false-positive and false-negative rates in driver nomination. When applied to a large study of primary melanomas, the method accurately identifies the known driver genes within the RTK-RAS pathway and nominates several rare variants as prime candidates for functional validation. A comprehensive evaluation of MAGPIE against existing tools has also been conducted leveraging the Cancer Genome Atlas data.

The BMP inhibitor Coco reactivates breast cancer cells at lung metastatic sites.

The mechanistic underpinnings of metastatic dormancy and reactivation are poorly understood. A gain-of-function cDNA screen reveals that Coco, a secreted antagonist of TGF-ß ligands, induces dormant breast cancer cells to undergo reactivation in the lung. Mechanistic studies indicate that Coco exerts this effect by blocking lung-derived BMP ligands. Whereas Coco enhances the manifestation of traits associated with cancer stem cells, BMP signaling suppresses it. Coco induces a discrete gene expression signature, which is strongly associated with metastatic relapse to the lung, but not to the bone or brain in patients. Experiments in mouse models suggest that these latter organs contain niches devoid of bioactive BMP. These findings reveal that metastasis-initiating cells need to overcome organ-specific antimetastatic signals in order to undergo reactivation.

Identifying somatic fingerprints of cancers defined by germline and environmental risk factors.

Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.

Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach.

Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning.

Integration of peripheral blood- and tissue-based biomarkers of response to immune checkpoint blockade in urothelial carcinoma.

As predictive biomarkers of response to immune checkpoint inhibitors (ICIs) remain a major unmet clinical need in patients with urothelial carcinoma (UC), we sought to identify tissue-based immune biomarkers of clinical benefit to ICIs using multiplex immunofluorescence and to integrate these findings with previously identified peripheral blood biomarkers of response. Fifty-five pretreatment and 12 paired on-treatment UC specimens were identified from patients treated with nivolumab with or without ipilimumab. Whole tissue sections were stained with a 12-plex mIF panel, including CD8, PD-1/CD279, PD-L1/CD274, CD68, CD3, CD4, FoxP3, TCF1/7, Ki67, LAG-3, MHC-II/HLA-DR, and pancytokeratin+SOX10 to identify over three million cells. Immune tissue densities were compared to progression-free survival (PFS) and best overall response (BOR) by RECIST version 1.1. Correlation coefficients were calculated between tissue-based and circulating immune populations. The frequency of intratumoral CD3+ LAG-3+ cells was higher in responders compared to nonresponders (p = 0.0001). LAG-3+ cellular aggregates were associated with response, including CD3+ LAG-3+ in proximity to CD3+ (p = 0.01). Exploratory multivariate modeling showed an association between intratumoral CD3+ LAG-3+ cells and improved PFS independent of prognostic clinical factors (log HR -7.0; 95% confidence interval [CI] -12.7 to -1.4), as well as established biomarkers predictive of ICI response (log HR -5.0; 95% CI -9.8 to -0.2). Intratumoral LAG-3+ immune cell populations warrant further study as a predictive biomarker of clinical benefit to ICIs. Differences in LAG-3+ lymphocyte populations across the intratumoral and peripheral compartments may provide complementary information that could inform the future development of multimodal composite biomarkers of ICI response. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Comparative genomics of primary prostate cancer and paired metastases: insights from 12 molecular case studies.

Primary prostate cancer (PCa) can show marked molecular heterogeneity. However, systematic analyses comparing primary PCa and matched metastases in individual patients are lacking. We aimed to address the molecular aspects of metastatic progression while accounting for the heterogeneity of primary PCa. In this pilot study, we collected 12 radical prostatectomy (RP) specimens from men who subsequently developed metastatic castration-resistant prostate cancer (mCRPC). We used histomorphology (Gleason grade, focus size, stage) and immunohistochemistry (IHC) (ERG and p53) to identify independent tumors and/or distinct subclones of primary PCa. We then compared molecular profiles of these primary PCa areas to matched metastatic samples using whole-exome sequencing (WES) and amplicon-based DNA and RNA sequencing. Based on combined pathology and molecular analysis, seven (58%) RP specimens harbored monoclonal and topographically continuous disease, albeit with some degree of intratumor heterogeneity; four (33%) specimens showed true multifocal disease; and one displayed monoclonal disease with discontinuous topography. Early (truncal) events in primary PCa included SPOP p.F133V (one patient), BRAF p.K601E (one patient), and TMPRSS2:ETS rearrangements (eight patients). Activating AR alterations were seen in nine (75%) mCRPC patients, but not in matched primary PCa. Hotspot TP53 mutations, found in metastases from three patients, were readily present in matched primary disease. Alterations in genes encoding epigenetic modifiers were observed in several patients (either shared between primary foci and metastases or in metastatic samples only). WES-based phylogenetic reconstruction and/or clonality scores were consistent with the index focus designated by pathology review in six out of nine (67%) cases. The three instances of discordance pertained to monoclonal, topographically continuous tumors, which would have been considered as unique disease in routine practice. Overall, our results emphasize pathologic and molecular heterogeneity of primary PCa, and suggest that comprehensive IHC-assisted pathology review and genomic analysis are highly concordant in nominating the 'index' primary PCa area. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations.

BACKGROUND: Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The "rare variant hypothesis" proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale. OBJECTIVES: In this study, we investigated associations between rare variants and 14 cancer types. METHODS: We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG). RESULTS: We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer). CONCLUSIONS: Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.

Using the "Hidden" genome to improve classification of cancer types.

It is increasingly common clinically for cancer specimens to be examined using techniques that identify somatic mutations. In principle, these mutational profiles can be used to diagnose the tissue of origin, a critical task for the 3% to 5% of tumors that have an unknown primary site. Diagnosis of primary site is also critical for screening tests that employ circulating DNA. However, most mutations observed in any new tumor are very rarely occurring mutations, and indeed the preponderance of these may never have been observed in any previous recorded tumor. To create a viable diagnostic tool we need to harness the information content in this "hidden genome" of variants for which no direct information is available. To accomplish this we propose a multilevel meta-feature regression to extract the critical information from rare variants in the training data in a way that permits us to also extract diagnostic information from any previously unobserved variants in the new tumor sample. A scalable implementation of the model is obtained by combining a high-dimensional feature screening approach with a group-lasso penalized maximum likelihood approach based on an equivalent mixed-effect representation of the multilevel model. We apply the method to the Cancer Genome Atlas whole-exome sequencing data set including 3702 tumor samples across seven common cancer sites. Results show that our multilevel approach can harness substantial diagnostic information from the hidden genome.

Human genes differ by their UV sensitivity estimated through analysis of UV-induced silent mutations in melanoma.

We hypothesized that human genes differ by their sensitivity to ultraviolet (UV) exposure. We used somatic mutations detected by genome-wide screens in melanoma and reported in the Catalog Of Somatic Mutations In Cancer. As a measure of UV sensitivity, we used the number of silent mutations generated by C>T transitions in pyrimidine dimers of a given transcript divided by the number of potential sites for this type of mutations in the transcript. We found that human genes varied by UV sensitivity by two orders of magnitude. We noted that the melanoma-associated tumor suppressor gene CDKN2A was among the top five most UV-sensitive genes in the human genome. Melanoma driver genes have a higher UV-sensitivity compared with other genes in the human genome. The difference was more prominent for tumor suppressors compared with oncogene. The results of this study suggest that differential sensitivity of human transcripts to UV light may explain melanoma specificity of some driver genes. Practical significance of the study relates to the fact that differences in UV sensitivity among human genes need to be taken into consideration whereas predicting melanoma-associated genes by the number of somatic mutations detected in a given gene.

Using association signal annotations to boost similarity network fusion.

MOTIVATION: Recent technology developments have made it possible to generate various kinds of omics data, which provides opportunities to better solve problems such as disease subtyping or disease mapping using more comprehensive omics data jointly. Among many developed data-integration methods, the similarity network fusion (SNF) method has shown a great potential to identify new disease subtypes through separating similar subjects using multi-omics data. SNF effectively fuses similarity networks with pairwise patient similarity measures from different types of omics data into one fused network using both shared and complementary information across multiple types of omics data. RESULTS: In this article, we proposed an association-signal-annotation boosted similarity network fusion (ab-SNF) method, adding feature-level association signal annotations as weights aiming to up-weight signal features and down-weight noise features when constructing subject similarity networks to boost the performance in disease subtyping. In various simulation studies, the proposed ab-SNF outperforms the original SNF approach without weights. Most importantly, the improvement in the subtyping performance due to association-signal-annotation weights is amplified in the integration process. Applications to somatic mutation data, DNA methylation data and gene expression data of three cancer types from The Cancer Genome Atlas project suggest that the proposed ab-SNF method consistently identifies new subtypes in each cancer that more accurately predict patient survival and are more biologically meaningful. AVAILABILITY AND IMPLEMENTATION: The R package abSNF is freely available for downloading from https://github.com/pfruan/abSNF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.

Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data. Therefore, the aim of this article is to develop a fully Bayesian latent variable method (called iClusterBayes) that can jointly model omics data of continuous and discrete data types for identification of tumor subtypes and relevant omics features. Specifically, the proposed method uses a few latent variables to capture the inherent structure of multiple omics data sets to achieve joint dimension reduction. As a result, the tumor samples can be clustered in the latent variable space and relevant omics features that drive the sample clustering are identified through Bayesian variable selection. This method significantly improve on the existing integrative clustering method iClusterPlus in terms of statistical inference and computational speed. By analyzing TCGA and simulated data sets, we demonstrate the excellent performance of the proposed method in revealing clinically meaningful tumor subtypes and driver omics features.

Lessons learned from routine, targeted assessment of liquid biopsies for EGFR T790M resistance mutation in patients with EGFR mutant lung cancers.

Background: Plasma cfDNA evaluation at acquired resistance to targeted therapies in lung cancer is routine, however, reports of extended clinical application and pitfalls in laboratory practice are still limited. In this study we describe our experience with cfDNA testing using EGFR T790M as a prototype.Methods: Patients with metastatic EGFR-mutant NSCLC patients who underwent plasma EGFR T790M testing at acquired resistance to EGFR tyrosine kinase inhibitors (EGFR-TKI) from January 2016 through August 2017 were identified. Molecular laboratory records were reviewed to assess performance of testing by digital PCR, concordance between plasma and tissue testing, turnaround time (TAT), plasma T790M variant allele frequency (VAF), and its correlations with metastatic sites and clinical outcomes.Results: 177 patients underwent T790M cfDNA testing during this period. Plasma T790M was positive in 32% of patients. The median TAT was shorter for plasma T790M compared to tissue PCR (9 vs. 15 days, p < .0001), and led to osimertinib use in 84% of positive patients. In 52 patients with plasma and tissue T790M evaluation, the concordance was 77%. Plasma T790M VAF did not correlate with time to osimertinib discontinuation (p = .4). Plasma T790M status correlated with a higher number of metastatic sites (4 vs. 3, p < .001) and bone metastases (p = .0002).Conclusion: Plasma EGFR T790M testing had shorter TAT compared to tissue testing, however, it was longer than anticipated. Test sensitivity is higher in patients with osseous metastases and with higher metastatic burden suggesting a more limited role for early detection. T790M VAF was not associated with clinical outcomes.

Integrated genomic characterization of endometrial carcinoma.

We performed an integrated genomic, transcriptomic and proteomic characterization of 373 endometrial carcinomas using array- and sequencing-based technologies. Uterine serous tumours and ∼25% of high-grade endometrioid tumours had extensive copy number alterations, few DNA methylation changes, low oestrogen receptor/progesterone receptor levels, and frequent TP53 mutations. Most endometrioid tumours had few copy number alterations or TP53 mutations, but frequent mutations in PTEN, CTNNB1, PIK3CA, ARID1A and KRAS and novel mutations in the SWI/SNF chromatin remodelling complex gene ARID5B. A subset of endometrioid tumours that we identified had a markedly increased transversion mutation frequency and newly identified hotspot mutations in POLE. Our results classified endometrial cancers into four categories: POLE ultramutated, microsatellite instability hypermutated, copy-number low, and copy-number high. Uterine serous carcinomas share genomic features with ovarian serous and basal-like breast carcinomas. We demonstrated that the genomic features of endometrial carcinomas permit a reclassification that may affect post-surgical adjuvant treatment for women with aggressive tumours.

Variance prior specification for a basket trial design using Bayesian hierarchical modeling.

BACKGROUND: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. METHODS: A common approach used to capture the correlated binary endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a non-randomized basket trial and investigate three popular prior specifications: an inverse-gamma prior on the basket-level variance, a uniform prior and half-t prior on the basket-level standard deviation. RESULTS: From our simulation study, we can see that the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero (≤0.5) , this can lead to unacceptably high false-positive rates (≥40%) in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that places sufficient mass in the tail, such as the uniform or half-t prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior and the scale parameter of the half-t prior must be larger than 1. CONCLUSION: Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior or half-t prior on the standard deviation.

FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing.

Allele-specific copy number analysis (ASCN) from next generation sequencing (NGS) data can greatly extend the utility of NGS beyond the identification of mutations to precisely annotate the genome for the detection of homozygous/heterozygous deletions, copy-neutral loss-of-heterozygosity (LOH), allele-specific gains/amplifications. In addition, as targeted gene panels are increasingly used in clinical sequencing studies for the detection of 'actionable' mutations and copy number alterations to guide treatment decisions, accurate, tumor purity-, ploidy- and clonal heterogeneity-adjusted integer copy number calls are greatly needed to more reliably interpret NGS-based cancer gene copy number data in the context of clinical sequencing. We developed FACETS, an ASCN tool and open-source software with a broad application to whole genome, whole-exome, as well as targeted panel sequencing platforms. It is a fully integrated stand-alone pipeline that includes sequencing BAM file post-processing, joint segmentation of total- and allele-specific read counts, and integer copy number calls corrected for tumor purity, ploidy and clonal heterogeneity, with comprehensive output and integrated visualization. We demonstrate the application of FACETS using The Cancer Genome Atlas (TCGA) whole-exome sequencing of lung adenocarcinoma samples. We also demonstrate its application to a clinical sequencing platform based on a targeted gene panel.

Hormone receptor status of a first primary breast cancer predicts contralateral breast cancer risk in the WECARE study population.

BACKGROUND: Previous population-based studies have described first primary breast cancer tumor characteristics and their association with contralateral breast cancer (CBC) risk. However, information on influential covariates such as treatment, family history of breast cancer, and BRCA1/2 mutation carrier status was not available. In a large, population-based, case-control study, we evaluated whether tumor characteristics of the first primary breast cancer are associated with risk of developing second primary asynchronous CBC, overall and in subgroups of interest, including among BRCA1/2 mutation non-carriers, women who are not treated with tamoxifen, and women without a breast cancer family history. METHODS: The Women's Environmental Cancer and Radiation Epidemiology Study is a population-based case-control study of 1521 CBC cases and 2212 individually-matched controls with unilateral breast cancer. Detailed information about breast cancer risk factors, treatment for and characteristics of first tumors, including estrogen receptor (ER) and progesterone receptor (PR) status, was obtained by telephone interview and medical record abstraction. Multivariable risk ratios (RRs) and 95% confidence intervals (CIs) were estimated in conditional logistic regression models, adjusting for demographics, treatment, and personal medical and family history. A subset of women was screened for BRCA1/2 mutations. RESULTS: Lobular histology of the first tumor was associated with a 30% increase in CBC risk (95% CI 1.0-1.6). Compared to women with ER+/PR+ first tumors, those with ER-/PR- tumors had increased risk of CBC (RR = 1.4, 95% CI 1.1-1.7). Notably, women with ER-/PR- first tumors were more likely to develop CBC with the ER-/PR- phenotype (RR = 5.4, 95% CI 3.0-9.5), and risk remained elevated in multiple subgroups: BRCA1/2 mutation non-carriers, women younger than 45 years of age, women without a breast cancer family history, and women who were not treated with tamoxifen. CONCLUSIONS: Having a hormone receptor negative first primary breast cancer is associated with increased risk of CBC. Women with ER-/PR- primary tumors were more likely to develop ER-/PR- CBC, even after excluding BRCA1/2 mutation carriers. Hormone receptor status, which is routinely evaluated in breast tumors, may be used clinically to determine treatment protocols and identify patients who may benefit from increased surveillance for CBC.

An efficient basket trial design.

The landscape for early phase cancer clinical trials is changing dramatically because of the advent of targeted therapy. Increasingly, new drugs are designed to work against a target such as the presence of a specific tumor mutation. Because typically only a small proportion of cancer patients will possess the mutational target, but the mutation is present in many different cancers, a new class of basket trials is emerging, whereby the drug is tested simultaneously in different baskets, that is, subgroups of different tumor types. Investigators desire not only to test whether the drug works but also to determine which types of tumors are sensitive to the drug. A natural strategy is to conduct parallel trials, with the drug 's effectiveness being tested separately, using for example, the popular Simon two-stage design independently in each basket. The work presented is motivated by the premise that the efficiency of this strategy can be improved by assessing the homogeneity of the baskets ' response rates at an interim analysis and aggregating the baskets in the second stage if the results suggest the drug might be effective in all or most baskets. Via simulations, we assess the relative efficiencies of the two strategies. Because the operating characteristics depend on how many tumor types are sensitive to the drug, there is no uniformly efficient strategy. However, our investigation demonstrates that substantial efficiencies are possible if the drug works in most or all baskets, at the cost of modest losses of power if the drug works in only a single basket. Copyright © 2017 John Wiley & Sons, Ltd.

Selumetinib-enhanced radioiodine uptake in advanced thyroid cancer.

BACKGROUND: Metastatic thyroid cancers that are refractory to radioiodine (iodine-131) are associated with a poor prognosis. In mouse models of thyroid cancer, selective mitogen-activated protein kinase (MAPK) pathway antagonists increase the expression of the sodium-iodide symporter and uptake of iodine. Their effects in humans are not known. METHODS: We conducted a study to determine whether the MAPK kinase (MEK) 1 and MEK2 inhibitor selumetinib (AZD6244, ARRY-142886) could reverse refractoriness to radioiodine in patients with metastatic thyroid cancer. After stimulation with thyrotropin alfa, dosimetry with iodine-124 positron-emission tomography (PET) was performed before and 4 weeks after treatment with selumetinib (75 mg twice daily). If the second iodine-124 PET study indicated that a dose of iodine-131 of 2000 cGy or more could be delivered to the metastatic lesion or lesions, therapeutic radioiodine was administered while the patient was receiving selumetinib. RESULTS: Of 24 patients screened for the study, 20 could be evaluated. The median age was 61 years (range, 44 to 77), and 11 patients were men. Nine patients had tumors with BRAF mutations, and 5 patients had tumors with mutations of NRAS. Selumetinib increased the uptake of iodine-124 in 12 of the 20 patients (4 of 9 patients with BRAF mutations and 5 of 5 patients with NRAS mutations). Eight of these 12 patients reached the dosimetry threshold for radioiodine therapy, including all 5 patients with NRAS mutations. Of the 8 patients treated with radioiodine, 5 had confirmed partial responses and 3 had stable disease; all patients had decreases in serum thyroglobulin levels (mean reduction, 89%). No toxic effects of grade 3 or higher attributable by the investigators to selumetinib were observed. One patient received a diagnosis of myelodysplastic syndrome more than 51 weeks after radioiodine treatment, with progression to acute leukemia. CONCLUSIONS: Selumetinib produces clinically meaningful increases in iodine uptake and retention in a subgroup of patients with thyroid cancer that is refractory to radioiodine; the effectiveness may be greater in patients with RAS-mutant disease. (Funded by the American Thyroid Association and others; ClinicalTrials.gov number, NCT00970359.).

Genomic landscape of adenoid cystic carcinoma of the breast.

Adenoid cystic carcinoma (AdCC) is a rare type of triple-negative breast cancer (TNBC) characterized by the presence of the MYB-NFIB fusion gene. The molecular underpinning of breast AdCCs other than the MYB-NFIB fusion gene remains largely unexplored. Here we sought to define the repertoire of somatic genetic alterations of breast AdCCs. We performed whole-exome sequencing, followed by orthogonal validation, of 12 breast AdCCs to determine the landscape of somatic mutations and gene copy number alterations. Fluorescence in situ hybridization and reverse-transcription PCR were used to define the presence of MYB gene rearrangements and MYB-NFIB chimeric transcripts. Unlike common forms of TNBC, we found that AdCCs have a low mutation rate (0.27 non-silent mutations/Mb), lack mutations in TP53 and PIK3CA and display a heterogeneous constellation of known cancer genes affected by somatic mutations, including MYB, BRAF, FBXW7, SMARCA5, SF3B1 and FGFR2. MYB and TLN2 were affected by somatic mutations in two cases each. Akin to salivary gland AdCCs, breast AdCCs were found to harbour mutations targeting chromatin remodelling, cell adhesion, RNA biology, ubiquitination and canonical signalling pathway genes. We observed that, although breast AdCCs had rather simple genomes, they likely display intra-tumour genetic heterogeneity at diagnosis. Taken together, these findings demonstrate that the mutational burden and mutational repertoire of breast AdCCs are more similar to those of salivary gland AdCCs than to those of other types of TNBCs, emphasizing the importance of histological subtyping of TNBCs. Furthermore, our data provide direct evidence that AdCCs harbour a distinctive mutational landscape and genomic structure, irrespective of the disease site of origin.