Explore the vast range of titles available.

Metastasis is the main cause of death for patients with breast cancer. Many studies have characterized the genomic landscape of breast cancer during its early stages. However, there is evidence that genomic alterations are acquired during the evolution of cancers from their early to late stages, and that the genomic landscape of early cancers is not representative of that of lethal cancers 1 – 7 . Here we investigated the landscape of somatic alterations in 617 metastatic breast cancers. Nine driver genes ( TP53 , ESR1 , GATA3 , KMT2C , NCOR1 , AKT1 , NF1 , RIC8A and RB1 ) were more frequently mutated in metastatic breast cancers that expressed hormone receptors (oestrogen and/or progesterone receptors; HR + ) but did not have high levels of HER2 (HER2 − ; n  = 381), when compared to early breast cancers from The Cancer Genome Atlas. In addition, 18 amplicons were more frequently observed in HR + /HER2 − metastatic breast cancers. These cancers showed an increase in mutational signatures S2, S3, S10, S13 and S17. Among the gene alterations that were enriched in HR + /HER2 − metastatic breast cancers, mutations in TP53 , RB1 and NF1 , together with S10, S13 and S17, were associated with poor outcome. Metastatic triple-negative breast cancers showed an increase in the frequency of somatic biallelic loss-of-function mutations in genes related to homologous recombination DNA repair, compared to early triple-negative breast cancers (7% versus 2%). Finally, metastatic breast cancers showed an increase in mutational burden and clonal diversity compared to early breast cancers. Thus, the genomic landscape of metastatic breast cancer is enriched in clinically relevant genomic alterations and is more complex than that of early breast cancer. The identification of genomic alterations associated with poor outcome will allow earlier and better selection of patients who require the use of treatments that are still in clinical trials. The genetic complexity observed in advanced breast cancer suggests that such treatments should be introduced as early as possible in the disease course. Patient data from six clinical trials are used to compare the genomic landscapes of breast cancer metastases with those of primary tumours, revealing an increase in mutational burden and clonal diversity.

Therapy resistance and metastatic processes in prostate cancer (PCa) remain undefined, due to lack of experimental models that mimic different disease stages. We describe an androgen-dependent PCa patient-derived xenograft (PDX) model from treatment-naïve, soft tissue metastasis (PNPCa). RNA and whole-exome sequencing of the PDX tissue and organoids confirmed transcriptomic and genomic similarity to primary tumor. PNPCa harbors BRCA2 and CHD1 somatic mutations, shows an SPOP/FOXA1 -like transcriptomic signature and microsatellite instability, which occurs in 3% of advanced PCa and has never been modeled in vivo. Comparison of the treatment-naïve PNPCa with additional metastatic PDXs (BM18, LAPC9), in a medium-throughput organoid screen of FDA-approved compounds, revealed differential drug sensitivities. Multikinase inhibitors (ponatinib, sunitinib, sorafenib) were broadly effective on all PDX- and patient-derived organoids from advanced cases with acquired resistance to standard-of-care compounds. This proof-of-principle study may provide a preclinical tool to screen drug responses to standard-of-care and newly identified, repurposed compounds. To date, patients still succumb to cancer, due to tumors not responding to therapy or ultimately acquiring resistance. Here the authors show that by exploiting patient derived organoids and a treatment-naïve patient derived xenograft, patient therapy can be personalized.

Adenoid cystic carcinoma of the breast is a rare histological type of triple-negative breast cancer with an indolent clinical behavior, often driven by the MYB-NFIB fusion gene. Here we sought to define the repertoire of somatic genetic alterations in two adenoid cystic carcinomas associated with high-grade triple-negative breast cancer. The different components of each case were subjected to copy number profiling and massively parallel sequencing targeting all exons and selected regulatory and intronic regions of 488 genes. Reverse transcription PCR and fluorescence in situ hybridization were employed to investigate the presence of the MYB-NFIB translocation. The MYB-NFIB fusion gene was detected in both adenoid cystic carcinomas and their associated high-grade triple-negative breast cancer components. Although the distinct components of both cases displayed similar patterns of gene copy number alterations, massively parallel sequencing analysis revealed intratumor genetic heterogeneity. In case 1, progression from the trabecular adenoid cystic carcinoma to the high-grade triple-negative breast cancer was found to involve clonal shifts with enrichment of mutations affecting EP300, NOTCH1, ERBB2 and FGFR1 in the high-grade triple-negative breast cancer. In case 2, a clonal KMT2C mutation was present in the cribriform adenoid cystic carcinoma, solid adenoid cystic carcinoma and high-grade triple-negative breast cancer components, whereas a mutation affecting MYB was present only in the solid and high-grade triple-negative breast cancer areas and additional three mutations targeting STAG2, KDM6A and CDK12 were restricted to the high-grade triple-negative breast cancer. In conclusion, adenoid cystic carcinomas of the breast with high-grade transformation are underpinned by the MYB-NFIB fusion gene and, akin to other forms of cancer, may be constituted by a mosaic of cancer cell clones at diagnosis. The progression from adenoid cystic carcinoma to high-grade triple-negative breast cancer of no special type may involve the selection of neoplastic clones and/or the acquisition of additional genetic alterations.

Triple negative breast cancer represents a heterogeneous group of breast carcinomas, both at the histologic and genetic level. Although recent molecular studies have comprehensively characterized the genetic landscape of these tumors, few have integrated a detailed histologic examination into the analysis. In this study, we defined the genetic alterations in 39 triple negative breast cancers using a high-depth targeted massively parallel sequencing assay and correlated the findings with a detailed morphologic analysis. We obtained representative frozen tissue of primary triple negative breast cancers from patients treated at our institution between 2002 and 2010. We characterized tumors according to their histologic subtype and morphologic features. DNA was extracted from paired frozen primary tumor and normal tissue samples and was subjected to a targeted massively parallel sequencing platform comprising 229 cancer-associated genes common across all experiments. The average number of non-synonymous mutations was 3 (range 0–10) per case. The most frequent somatic alterations were mutations in TP53 (74%) and PIK3CA (10%) and MYC amplifications (26%). Triple negative breast cancers with apocrine differentiation less frequently harbored TP53 mutations (25%) and MYC gains (0%), and displayed a high mutation frequency in PIK3CA and other PI3K signaling pathway-related genes (75%). Using a targeted massively parallel sequencing platform, we identified the key somatic genetic alterations previously reported in triple negative breast cancers. Furthermore, our findings show that triple negative breast cancers with apocrine differentiation constitute a distinct subset, characterized by a high frequency of PI3K pathway alterations similar to luminal subtypes of breast cancer.

Resistance acquisition to androgen deprivation treatment and metastasis progression are a major clinical issue associated with prostate cancer (PCa). The role of stroma during disease progression is insufficiently defined. Using transcriptomic and proteomic analyses on differentially aggressive patient-derived xenografts (PDXs), we investigated whether PCa tumors predispose their microenvironment (stroma) to a metastatic gene expression pattern. RNA sequencing was performed on the PCa PDXs BM18 (castration-sensitive) and LAPC9 (castration-resistant), representing different disease stages. Using organism-specific reference databases, the human-specific transcriptome (tumor) was identified and separated from the mouse-specific transcriptome (stroma). To identify proteomic changes in the tumor (human) versus the stroma (mouse), we performed human/mouse cell separation and subjected protein lysates to quantitative Tandem Mass Tag labeling and mass spectrometry. Tenascin C (TNC) was among the most abundant stromal genes, modulated by androgen levels in vivo and highly expressed in castration-resistant LAPC9 PDX. The tissue microarray of primary PCa samples (n = 210) showed that TNC is a negative prognostic marker of the clinical progression to recurrence or metastasis. Stroma markers of osteoblastic PCa bone metastases seven-up signature were induced in the stroma by the host organism in metastatic xenografts, indicating conserved mechanisms of tumor cells to induce a stromal premetastatic signature. A 50-gene list stroma signature was identified based on androgen-dependent responses, which shows a linear association with the Gleason score, metastasis progression and progression-free survival. Our data show that metastatic PCa PDXs, which differ in androgen sensitivity, trigger differential stroma responses, which show the metastasis risk stratification and prognostic biomarker potential.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Proper treatment of tuberculosis requires a combination of drugs to which the causative organism is susceptible. In this report, genotypic assessment of antimicrobial susceptibility of more than 10,000 M. tuberculosis isolates was correlated with phenotypic test results.

Background Estrogen receptors alpha (ERα) and beta (ERβ) are transcription factors (TFs) that mediate estrogen signaling and define the hormone-responsive phenotype of breast cancer (BC). The two receptors can be found co-expressed and play specific, often opposite, roles, with ERβ being able to modulate the effects of ERα on gene transcription and cell proliferation. ERβ is frequently lost in BC, where its presence generally correlates with a better prognosis of the disease. The identification of the genomic targets of ERβ in hormone-responsive BC cells is thus a critical step to elucidate the roles of this receptor in estrogen signaling and tumor cell biology. Results Expression of full-length ERβ in hormone-responsive, ERα-positive MCF-7 cells resulted in a marked reduction in cell proliferation in response to estrogen and marked effects on the cell transcriptome. By ChIP-Seq we identified 9702 ERβ and 6024 ERα binding sites in estrogen-stimulated cells, comprising sites occupied by either ERβ, ERα or both ER subtypes. A search for TF binding matrices revealed that the majority of the binding sites identified comprise one or more Estrogen Response Element and the remaining show binding matrixes for other TFs known to mediate ER interaction with chromatin by tethering, including AP2, E2F and SP1. Of 921 genes differentially regulated by estrogen in ERβ+ vs ERβ- cells, 424 showed one or more ERβ site within 10 kb. These putative primary ERβ target genes control cell proliferation, death, differentiation, motility and adhesion, signal transduction and transcription, key cellular processes that might explain the biological and clinical phenotype of tumors expressing this ER subtype. ERβ binding in close proximity of several miRNA genes and in the mitochondrial genome, suggests the possible involvement of this receptor in small non-coding RNA biogenesis and mitochondrial genome functions. Conclusions Results indicate that the vast majority of the genomic targets of ERβ can bind also ERα, suggesting that the overall action of ERβ on the genome of hormone-responsive BC cells depends mainly on the relative concentration of both ERs in the cell.

Background Qualitative and quantitative analysis of small non-coding RNAs by next generation sequencing (smallRNA-Seq) represents a novel technology increasingly used to investigate with high sensitivity and specificity RNA population comprising microRNAs and other regulatory small transcripts. Analysis of smallRNA-Seq data to gather biologically relevant information, i.e. detection and differential expression analysis of known and novel non-coding RNAs, target prediction, etc., requires implementation of multiple statistical and bioinformatics tools from different sources, each focusing on a specific step of the analysis pipeline. As a consequence, the analytical workflow is slowed down by the need for continuous interventions by the operator, a critical factor when large numbers of datasets need to be analyzed at once. Results We designed a novel modular pipeline (iMir) for comprehensive analysis of smallRNA-Seq data, comprising specific tools for adapter trimming, quality filtering, differential expression analysis, biological target prediction and other useful options by integrating multiple open source modules and resources in an automated workflow. As statistics is crucial in deep-sequencing data analysis, we devised and integrated in iMir tools based on different statistical approaches to allow the operator to analyze data rigorously. The pipeline created here proved to be efficient and time-saving than currently available methods and, in addition, flexible enough to allow the user to select the preferred combination of analytical steps. We present here the results obtained by applying this pipeline to analyze simultaneously 6 smallRNA-Seq datasets from either exponentially growing or growth-arrested human breast cancer MCF-7 cells, that led to the rapid and accurate identification, quantitation and differential expression analysis of ~450 miRNAs, including several novel miRNAs and isomiRs, as well as identification of the putative mRNA targets of differentially expressed miRNAs. In addition, iMir allowed also the identification of ~70 piRNAs (piwi-interacting RNAs), some of which differentially expressed in proliferating vs growth arrested cells. Conclusion The integrated data analysis pipeline described here is based on a reliable, flexible and fully automated workflow, useful to rapidly and efficiently analyze high-throughput smallRNA-Seq data, such as those produced by the most recent high-performance next generation sequencers. iMir is available at http://www.labmedmolge.unisa.it/inglese/research/imir .

Therapy resistance and metastatic processes in prostate cancer (PCa) remain undefined, due to lack of experimental models that mimic different disease stages. We describe a novel androgen-dependent PCa patient-derived xenograft (PDX) model from treatment-naive, soft tissue metastasis (PNPCa). RNA and whole-exome sequencing of the PDX tissue and organoids confirmed transcriptomic and genomic similarity to primary tumor. PNPCa harbours BRCA2 and CHD1 somatic mutations, shows an SPOP-FOXA1-like transcriptomic signature and microsatellite instability, which occurs in 3% of advanced PCa and has never been modelled in vivo. Comparison of the treatment-naive PNPCa with additional metastatic PDXs (BM18, LAPC9), in a medium-throughput organoid screen of FDA-approved compounds, revealed differential drug sensitivities. Multikinase inhibitors (ponatinib, sunitinib, sorafenib) were broadly effective on all PDX- and patient-derived organoids from advanced cases with acquired resistance to standard-of-care compounds. This proof-of-principle study may provide a preclinical tool to screen drug responses to standard-of-care and newly identified, repurposed compounds.