Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction.

Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC-TCGA (International Cancer Genome Consortium-The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.

Mutation of SIVA, a candidate metastasis gene identified from clonally related bilateral breast cancers, promotes breast cancer cell spread in vitro and in vivo.

Metastasis is the most dreaded outcome after a breast cancer diagnosis, and little is known regarding what triggers or promotes breast cancer to spread distally, or how to prevent or eradicate metastasis effectively. Bilateral breast cancers are an uncommon form of breast cancers. In our study, a percentage of bilateral breast cancers were clonally related based on copy number variation profiling. Whole exome sequencing and comparative sequence analysis revealed that a limited number of somatic mutations were acquired in this "breast-to-breast" metastasis that might promote breast cancer distant spread. One somatic mutation acquired was SIVA-D160N that displayed pro-metastatic phenotypes in vivo and in vitro. Over-expression of SIVA-D160N promoted migration and invasion of human MB-MDA-231 breast cancer cells in vitro, consistent with a dominant negative interfering function. When introduced via tail vein injection, 231 cells over-expressing SIVA-D160N displayed enhanced distant spread on IVIS imaging. Over-expression of SIVA-D160N promoted invasion and anchorage independent growth of mouse 4T1 breast cancer cells in vitro. When introduced orthotopically via mammary fat pad injection in syngeneic Balb/c mice, over-expression of SIVA-D160N in 4T1 cells increased orthotopically implanted mammary gland tumor growth as well as liver metastasis. Clonally related bilateral breast cancers represented a novel system to investigate metastasis and revealed a role of SIVA-D160N in breast cancer metastasis. Further characterization and understanding of SIVA function, and that of its interacting proteins, may elucidate mechanisms of breast cancer metastasis, providing clinically useful biomarkers and therapeutic targets.

HER2 heterogeneity and treatment response-associated profiles in HER2-positive breast cancer in the NCT02326974 clinical trial.

BACKGROUNDHER2-targeting therapies have great efficacy in HER2-positive breast cancer, but resistance, in part due to HER2 heterogeneity (HET), is a significant clinical challenge. We previously described that in a phase II neoadjuvant trastuzumab emtansine (T-DM1) and pertuzumab (P) clinical trial in early-stage HER2-positive breast cancer, none of the patients with HER2-HET tumors had pathologic complete response (pCR).METHODSTo investigate cellular and molecular differences among tumors according to HER2 heterogeneity and pCR, we performed RNA sequencing and ERBB2 FISH of 285 pretreatment and posttreatment tumors from 129 patients in this T-DM1+P neoadjuvant trial. A subset of cases was also subject to NanoString spatial digital profiling.RESULTSPretreatment tumors from patients with pCR had the highest level of ERBB2 mRNA and ERBB signaling. HER2 heterogeneity was associated with no pCR, basal-like features, and low ERBB2 expression yet high ERBB signaling sustained by activation of downstream pathway components. Residual tumors showed decreased HER2 protein levels and ERBB2 copy number heterogeneity and increased PI3K pathway enrichment and luminal features. HET tumors showed minimal treatment-induced transcriptomic changes compared with non-HET tumors. Immune infiltration correlated with pCR and HER2-HET status.CONCLUSIONResistance mechanisms in HET and non-HET tumors are distinct. HER2-targeting antibodies have limited efficacy in HET tumors. Our results support the stratification of patients based on HET status and the use of agents that target downstream components of the ERBB signaling pathway in patients with HET tumors.TRIAL REGISTRATIONClinicalTrials.gov NCT02326974.FUNDINGThis study was funded by Roche and the National Cancer Institute.

INHBA is a mediator of aggressive tumor behavior in HER2+ basal breast cancer.

BACKGROUND: Resistance to HER2-targeted therapeutics remains a significant clinical problem in HER2+ breast cancer patients with advanced disease. This may be particularly true for HER2+ patients with basal subtype disease, as recent evidence suggests they receive limited benefit from standard of care HER2-targeted therapies. Identification of drivers of resistance and aggressive disease that can be targeted clinically has the potential to impact patient outcomes. METHODS: We performed siRNA knockdown screens of genes differentially expressed between lapatinib-responsive and -resistant HER2+ breast cancer cells, which corresponded largely to luminal versus basal subtypes. We then validated hits in 2-d and 3-d cell culture systems. RESULTS: Knockdown of one of the genes, INHBA, significantly slowed growth and increased sensitivity to lapatinib in multiple basal HER2+ cell lines in both 2-d and 3-d cultures, but had no effect in luminal HER2+ cells. Loss of INHBA altered metabolism, eliciting a shift from glycolytic to oxidative phosphorylative metabolism, which was also associated with a decrease in tumor invasiveness. Analysis of breast cancer datasets showed that patients with HER2+ breast cancer and high levels of INHBA expression had worse outcomes than patients with low levels of INHBA expression. CONCLUSIONS: Our data suggest that INHBA is associated with aggressiveness of the basal subtype of HER2+ tumors, resulting in poor response to HER2-targeted therapy and an invasive phenotype. We hypothesize that targeting this pathway could be an effective therapeutic strategy to reduce invasiveness of tumor cells and to improve therapeutic response.

A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery.

The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.

Enzalutamide response in a panel of prostate cancer cell lines reveals a role for glucocorticoid receptor in enzalutamide resistant disease.

Representative in vitro model systems that accurately model response to therapy and allow the identification of new targets are important for improving our treatment of prostate cancer. Here we describe molecular characterization and drug testing in a panel of 20 prostate cancer cell lines. The cell lines cluster into distinct subsets based on RNA expression, which is largely driven by functional Androgen Receptor (AR) expression. KLK3, the AR-responsive gene that encodes prostate specific antigen, shows the greatest variability in expression across the cell line panel. Other common prostate cancer associated genes such as TMPRSS2 and ERG show similar expression patterns. Copy number analysis demonstrates that many of the most commonly gained (including regions containing TERC and MYC) and lost regions (including regions containing TP53 and PTEN) that were identified in patient samples by the TCGA are mirrored in the prostate cancer cell lines. Assessment of response to the anti-androgen enzalutamide shows a distinct separation of responders and non-responders, predominantly related to status of wild-type AR. Surprisingly, several AR-null lines responded to enzalutamide. These AR-null, enzalutamide-responsive cells were characterized by high levels of expression of glucocorticoid receptor (GR) encoded by NR3C1. Treatment of these cells with the anti-GR agent mifepristone showed that they were more sensitive to this drug than enzalutamide, as were several of the enzalutamide non-responsive lines. This is consistent with several recent reports that suggest that GR expression is an alternative signaling mechanism that can bypass AR blockade. This study reinforces the utility of large cell line panels for the study of cancer and identifies several cell lines that represent ideal models to study AR-null cells that have upregulated GR to sustain growth.

BET bromodomain inhibition blocks the function of a critical AR-independent master regulator network in lethal prostate cancer.

BET bromodomain inhibitors block prostate cancer cell growth at least in part through c-Myc and androgen receptor (AR) suppression. However, little is known about other transcriptional regulators whose suppression contributes to BET bromodomain inhibitor anti-tumor activity. Moreover, the anti-tumor activity of BET bromodomain inhibition in AR-independent castration-resistant prostate cancers (CRPC), whose frequency is increasing, is also unknown. Herein, we demonstrate that BET bromodomain inhibition blocks growth of a diverse set of CRPC cell models, including those that are AR-independent or in which c-Myc is not suppressed. To identify transcriptional regulators whose suppression accounts for these effects, we treated multiple CRPC cell lines with the BET bromodomain inhibitor JQ1 and then performed RNA-sequencing followed by Master Regulator computational analysis. This approach identified several previously unappreciated transcriptional regulators that are highly expressed in CRPC and whose suppression, via both transcriptional or post-translational mechanisms, contributes to the anti-tumor activity of BET bromodomain inhibitors.

Maintenance of MYC expression promotes de novo resistance to BET bromodomain inhibition in castration-resistant prostate cancer.

The BET bromodomain protein BRD4 is a chromatin reader that regulates transcription, including in cancer. In prostate cancer, specifically, the anti-tumor activity of BET bromodomain inhibition has been principally linked to suppression of androgen receptor (AR) function. MYC is a well-described BRD4 target gene in multiple cancer types, and prior work demonstrates that MYC plays an important role in promoting prostate cancer cell survival. Importantly, several BET bromodomain clinical trials are ongoing, including in prostate cancer. However, there is limited information about pharmacodynamic markers of response or mediators of de novo resistance. Using a panel of prostate cancer cell lines, we demonstrated that MYC suppression-rather than AR suppression-is a key determinant of BET bromodomain inhibitor sensitivity. Importantly, we determined that BRD4 was dispensable for MYC expression in the most resistant cell lines and that MYC RNAi + BET bromodomain inhibition led to additive anti-tumor activity in the most resistant cell lines. Our findings demonstrate that MYC suppression is an important pharmacodynamic marker of BET bromodomain inhibitor response and suggest that targeting MYC may be a promising therapeutic strategy to overcome de novo BET bromodomain inhibitor resistance in prostate cancer.

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines.

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects.

MHC class I loaded ligands from breast cancer cell lines: A potential HLA-I-typed antigen collection.

To build a catalog of peptides presented by breast cancer cells, we undertook systematic MHC class I immunoprecipitation followed by elution of MHC class I-loaded peptides in breast cancer cells. We determined the sequence of 3196 MHC class I ligands representing 1921 proteins from a panel of 20 breast cancer cell lines. After removing duplicate peptides, i.e., the same peptide eluted from more than one cell line, the total number of unique peptides was 2740. Of the unique peptides eluted, more than 1750 had been previously identified, and of these, sixteen have been shown to be immunogenic. Importantly, half of these immunogenic peptides were shared between different breast cancer cell lines. MHC class I binding probability was used to plot the distribution of the eluted peptides in accordance with the binding score for each breast cancer cell line. We also determined that the tested breast cancer cells presented 89 mutation-containing peptides and peptides derived from aberrantly translated genes, 7 of which were shared between four or two different cell lines. Overall, the high throughput identification of MHC class I-loaded peptides is an effective strategy for systematic characterization of cancer peptides, and could be employed for design of multi-peptide anticancer vaccines. SIGNIFICANCE: By employing proteomic analyses of eluted peptides from breast cancer cells, the current study has built an initial HLA-I-typed antigen collection for breast cancer research. It was also determined that immunogenic epitopes can be identified using established cell lines and that shared immunogenic peptides can be found in different cancer types such as breast cancer and leukemia. Importantly, out of 3196 eluted peptides that included duplicate peptides in different cells 89 peptides either contained mutation in their sequence or were derived from aberrant translation suggesting that mutation-containing epitopes are on the order of 2-3% in breast cancer cells. Finally, our results suggest that interfering with MHC class I function is one of the mechanisms of how tumor cells escape immune system attack.

Physiologically activated mammary fibroblasts promote postpartum mammary cancer.

Women diagnosed with breast cancer within 5 years of childbirth have poorer prognosis than nulliparous or pregnant women. Weaning-induced breast involution is implicated, as the collagen-rich, immunosuppressive microenvironment of the involuting mammary gland is tumor promotional in mice. To investigate the role of mammary fibroblasts, isolated mammary PDGFRα+ cells from nulliparous and postweaning mice were assessed for activation phenotype and protumorigenic function. Fibroblast activation during involution was evident by increased expression of fibrillar collagens, lysyl oxidase, Tgfb1, and Cxcl12 genes. The ability of mammary tumors to grow in an isogenic, orthotopic transplant model was increased when tumor cells were coinjected with involution-derived compared with nulliparous-derived mammary fibroblasts. Mammary tumors in the involution-fibroblast group had increased Ly6C+ monocytes at the tumor border, and decreased CD8+ T cell infiltration and tumor cell death. Ibuprofen treatment suppressed involution-fibroblast activation and tumor promotional capacity, concurrent with decreases in tumor Ly6C+ monocytes, and increases in intratumoral CD8+ T cell infiltration, granzyme levels, and tumor cell death. In total, our data identify a COX/prostaglandin E2 (PGE2)-dependent activated mammary fibroblast within the involuting mammary gland that displays protumorigenic, immunosuppressive activity, identifying fibroblasts as potential targets for the prevention and treatment of postpartum breast cancer.

Identification of shared TCR sequences from T cells in human breast cancer using emulsion RT-PCR.

Infiltration of T cells in breast tumors correlates with improved survival of patients with breast cancer, despite relatively few mutations in these tumors. To determine if T-cell specificity can be harnessed to augment immunotherapies of breast cancer, we sought to identify the alpha-beta paired T-cell receptors (TCRs) of tumor-infiltrating lymphocytes shared between multiple patients. Because TCRs function as heterodimeric proteins, we used an emulsion-based RT-PCR assay to link and amplify TCR pairs. Using this assay on engineered T-cell hybridomas, we observed ∼85% accurate pairing fidelity, although TCR recovery frequency varied. When we applied this technique to patient samples, we found that for any given TCR pair, the dominant alpha- or beta-binding partner comprised ∼90% of the total binding partners. Analysis of TCR sequences from primary tumors showed about fourfold more overlap in tumor-involved relative to tumor-free sentinel lymph nodes. Additionally, comparison of sequences from both tumors of a patient with bilateral breast cancer showed 10% overlap. Finally, we identified a panel of unique TCRs shared between patients' tumors and peripheral blood that were not found in the peripheral blood of controls. These TCRs encoded a range of V, J, and complementarity determining region 3 (CDR3) sequences on the alpha-chain, and displayed restricted V-beta use. The nucleotides encoding these shared TCR CDR3s varied, suggesting immune selection of this response. Harnessing these T cells may provide practical strategies to improve the shared antigen-specific response to breast cancer.

The Valley-of-Death: reciprocal sign epistasis constrains adaptive trajectories in a constant, nutrient limiting environment.

The fitness landscape is a powerful metaphor for describing the relationship between genotype and phenotype for a population under selection. However, empirical data as to the topography of fitness landscapes are limited, owing to difficulties in measuring fitness for large numbers of genotypes under any condition. We previously reported a case of reciprocal sign epistasis (RSE), where two mutations individually increased yeast fitness in a glucose-limited environment, but reduced fitness when combined, suggesting the existence of two peaks on the fitness landscape. We sought to determine whether a ridge connected these peaks so that populations founded by one mutant could reach the peak created by the other, avoiding the low-fitness "Valley-of-Death" between them. Sequencing clones after 250 generations of further evolution provided no evidence for such a ridge, but did reveal many presumptive beneficial mutations, adding to a growing body of evidence that clonal interference pervades evolving microbial populations.

Transcription restores DNA repair to heterochromatin, determining regional mutation rates in cancer genomes.

Somatic mutations in cancer are more frequent in heterochromatic and late-replicating regions of the genome. We report that regional disparities in mutation density are virtually abolished within transcriptionally silent genomic regions of cutaneous squamous cell carcinomas (cSCCs) arising in an XPC(-/-) background. XPC(-/-) cells lack global genome nucleotide excision repair (GG-NER), thus establishing differential access of DNA repair machinery within chromatin-rich regions of the genome as the primary cause for the regional disparity. Strikingly, we find that increasing levels of transcription reduce mutation prevalence on both strands of gene bodies embedded within H3K9me3-dense regions, and only to those levels observed in H3K9me3-sparse regions, also in an XPC-dependent manner. Therefore, transcription appears to reduce mutation prevalence specifically by relieving the constraints imposed by chromatin structure on DNA repair. We model this relationship among transcription, chromatin state, and DNA repair, revealing a new, personalized determinant of cancer risk.

Different selective pressures lead to different genomic outcomes as newly-formed hybrid yeasts evolve.

BACKGROUND: Interspecific hybridization occurs in every eukaryotic kingdom. While hybrid progeny are frequently at a selective disadvantage, in some instances their increased genome size and complexity may result in greater stress resistance than their ancestors, which can be adaptively advantageous at the edges of their ancestors' ranges. While this phenomenon has been repeatedly documented in the field, the response of hybrid populations to long-term selection has not often been explored in the lab. To fill this knowledge gap we crossed the two most distantly related members of the Saccharomyces sensu stricto group, S. cerevisiae and S. uvarum, and established a mixed population of homoploid and aneuploid hybrids to study how different types of selection impact hybrid genome structure. RESULTS: As temperature was raised incrementally from 31°C to 46.5°C over 500 generations of continuous culture, selection favored loss of the S. uvarum genome, although the kinetics of genome loss differed among independent replicates. Temperature-selected isolates exhibited greater inherent and induced thermal tolerance than parental species and founding hybrids, and also exhibited ethanol resistance. In contrast, as exogenous ethanol was increased from 0% to 14% over 500 generations of continuous culture, selection favored euploid S. cerevisiae x S. uvarum hybrids. Ethanol-selected isolates were more ethanol tolerant than S. uvarum and one of the founding hybrids, but did not exhibit resistance to temperature stress. Relative to parental and founding hybrids, temperature-selected strains showed heritable differences in cell wall structure in the forms of increased resistance to zymolyase digestion and Micafungin, which targets cell wall biosynthesis. CONCLUSIONS: This is the first study to show experimentally that the genomic fate of newly-formed interspecific hybrids depends on the type of selection they encounter during the course of evolution, underscoring the importance of the ecological theatre in determining the outcome of the evolutionary play.

Genotypic and phenotypic variation in Pseudomonas aeruginosa reveals signatures of secondary infection and mutator activity in certain cystic fibrosis patients with chronic lung infections.

Evolutionary adaptation of Pseudomonas aeruginosa to the cystic fibrosis lung is limited by genetic variation, which depends on rates of horizontal gene transfer and mutation supply. Because each may increase following secondary infection or mutator emergence, we sought to ascertain the incidence of secondary infection and genetic variability in populations containing or lacking mutators. Forty-nine strains collected over 3 years from 16 patients were phenotyped for antibiotic resistance and mutator status and were genotyped by repetitive-sequence PCR (rep-PCR), pulsed-field gel electrophoresis (PFGE), and multilocus sequence typing (MLST). Though phenotypic and genetic polymorphisms were widespread and clustered more strongly within than between longitudinal series, their distribution revealed instances of secondary infection. Sequence data, however, indicated that interlineage recombination predated initial strain isolation. Mutator series were more likely to be multiply antibiotic resistant, but not necessarily more variable in their nucleotide sequences, than nonmutators. One mutator and one nonmutator series were sequenced at mismatch repair loci and analyzed for gene content using DNA microarrays. Both were wild type with respect to mutL, but mutators carried an 8-bp mutS deletion causing a frameshift mutation. Both series lacked 126 genes encoding pilins, siderophores, and virulence factors whose inactivation has been linked to adaptation during chronic infection. Mutators exhibited loss of severalfold more genes having functions related to mobile elements, motility, and attachment. A 105-kb, 86-gene deletion was observed in one nonmutator that resulted in loss of virulence factors related to pyoverdine synthesis and elements of the multidrug efflux regulon. Diminished DNA repair activity may facilitate but not be absolutely required for rapid evolutionary change.

Hunger artists: yeast adapted to carbon limitation show trade-offs under carbon sufficiency.

As organisms adaptively evolve to a new environment, selection results in the improvement of certain traits, bringing about an increase in fitness. Trade-offs may result from this process if function in other traits is reduced in alternative environments either by the adaptive mutations themselves or by the accumulation of neutral mutations elsewhere in the genome. Though the cost of adaptation has long been a fundamental premise in evolutionary biology, the existence of and molecular basis for trade-offs in alternative environments are not well-established. Here, we show that yeast evolved under aerobic glucose limitation show surprisingly few trade-offs when cultured in other carbon-limited environments, under either aerobic or anaerobic conditions. However, while adaptive clones consistently outperform their common ancestor under carbon limiting conditions, in some cases they perform less well than their ancestor in aerobic, carbon-rich environments, indicating that trade-offs can appear when resources are non-limiting. To more deeply understand how adaptation to one condition affects performance in others, we determined steady-state transcript abundance of adaptive clones grown under diverse conditions and performed whole-genome sequencing to identify mutations that distinguish them from one another and from their common ancestor. We identified mutations in genes involved in glucose sensing, signaling, and transport, which, when considered in the context of the expression data, help explain their adaptation to carbon poor environments. However, different sets of mutations in each independently evolved clone indicate that multiple mutational paths lead to the adaptive phenotype. We conclude that yeasts that evolve high fitness under one resource-limiting condition also become more fit under other resource-limiting conditions, but may pay a fitness cost when those same resources are abundant.