Novel cancer gene variants and gene fusions of triple-negative breast cancers (TNBCs) reveal their molecular diversity conserved in the patient-derived xenograft (PDX) model

Screening Nonlinear miRNA Features of Breast Cancer by Using Ensemble Regularized Polynomial Logistic Regression

Differentiating breast cancer subtypes based on miRNA data helps doctors provide more personalized treatment plans for patients. This paper explored the interaction between miRNA pairs and developed a novel ensemble regularized polynomial logistic regression method for screening nonlinear features of breast cancer. Three different types of second-order polynomial logistic regression with elastic network penalty (SOPLR-EN) in which each type contains 10 identical models were integrated to determine the most suitable sample set for feature screening by using bootstrap sampling strategy. A single feature and 39 nonlinear features were obtained by screening features that appeared at least 15 times in 30 integrations and were involved in the classification of at least 4 subtypes. The second-order polynomial logistic regression with ridge penalty (SOPLR-R) built on screened feature set achieved 82.30% classification accuracy for distinguishing breast cancer subtypes, surpassing the performance of other six methods. Further, 11 nonlinear miRNA biomarkers were identified, and their significant relevance to breast cancer was illustrated through six types of biological analysis.

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.

Comparative RNA-Seq Analysis Revealed Tissue-Specific Splicing Variations during the Generation of the PDX Model

Tissue-specific gene expression generates fundamental differences in the function of each tissue and affects the characteristics of the tumors that are created as a result. However, it is unclear how much the tissue specificity is conserved during grafting of the primary tumor into an immune-compromised mouse model. Here, we performed a comparative RNA-seq analysis of four different primary-patient derived xenograft (PDX) tumors. The analysis revealed a conserved RNA biotype distribution of primary-PDX pairs, as revealed by previous works. Interestingly, we detected significant changes in the splicing pattern of PDX, which was mainly comprised of skipped exons. This was confirmed by splicing variant-specific RT-PCR analysis. On the other hand, the correlation analysis for the tissue-specific genes indicated overall strong positive correlations between the primary and PDX tumor pairs, with the exception of gastric cancer cases, which showed an inverse correlation. These data propose a tissue-specific change in splicing events during PDX formation as a variable factor that affects primary-PDX integrity.

Antibody-mediated blockade for galectin-3 binding protein in tumor secretome abrogates PDAC metastasis

The major challenges in pancreatic ductal adenocarcinoma (PDAC) management are local or distant metastasis and limited targeted therapeutics to prevent it. To identify a druggable target in tumor secretome and to explore its therapeutic intervention, we performed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic analysis of tumors obtained from a patient-derived xenograft model of PDAC. Galectin-3 binding protein (Gal-3BP) is identified as a highly secreted protein, and its overexpression is further validated in multiple PDAC tumors and primary cells. Knockdown and exogenous treatment of Gal-3BP showed that it is required for PDAC cell proliferation, migration, and invasion. Mechanistically, we revealed that Gal-3BP enhances galectin-3-mediated epidermal growth factor receptor signaling, leading to increased cMyc and epithelial-mesenchymal transition. To explore the clinical impact of these findings, two antibody clones were developed, and they profoundly abrogated the metastasis of PDAC cells in vivo. Altogether, our data demonstrate that Gal-3BP is an important therapeutic target in PDAC, and we propose its blockade by antibody as a therapeutic option for suppressing PDAC metastasis.

HIV-1 Drug Resistance Assay Using Ion Torrent Next Generation Sequencing and On-Instrument End-to-End Analysis Software

HIV-1 antiretroviral therapy management requires sequencing the protease, reverse transcriptase, and integrase portions of the HIV-1 pol gene. Most resistance testing is performed with Sanger sequencing, which has limited ability to detect minor variants. Next generation sequencing (NGS) platforms enable variant detection at frequencies as low as 1% allowing for earlier detection of resistance and modification of therapy. Implementation of NGS assays in the clinical laboratory is hindered by complicated assay design, cumbersome wet bench procedures, and the complexity of data analysis and bioinformatics. We developed a complete NGS protocol and companion analysis and reporting pipeline using AmpliSeq multiplex PCR, Ion Torrent S5 XL sequencing, and Stanford's HIVdb resistance algorithm. Implemented as a Torrent Suite software plugin, the pipeline runs automatically after sequencing. An optimum variant frequency threshold of 10% was determined by comparing Sanger sequences of archived samples from ViroSeq testing, resulting in a sensitivity of 98.2% and specificity of 99.0%. The majority (91%) of drug resistance mutations were detected by both Sanger and NGS, with 1.7% only by Sanger and 7.3% only by NGS. Variant calls were highly reproducible and there was no cross-reactivity to VZV, HBV, CMV, EBV, and HCV. The limit of detection was 500 copies/mL. The NGS assay performance was comparable to ViroSeq Sanger sequencing and has several advantages, including a publicly available end-to-end analysis and reporting plugin. The assay provides a straightforward path for implementation of NGS for HIV drug resistance testing in the laboratory setting without additional investment in bioinformatics infrastructure and resources.

Aberrant Transcript Usage Is Associated with Homologous Recombination Deficiency and Predicts Therapeutic Response

BRCA1/2 mutations account for only a small fraction of homologous recombination (HR) deficiency (HRD) cases. Recently developed genomic HRD (gHRD) tests suffer confounding factors that cause low precision in predicting samples that will respond to PARP inhibitors and DNA damaging agents. Here we present molecular and clinical evidence of transcriptional HRD (tHRD) that is based on aberrant transcript usage (aTU) of minor isoforms. Specifically, increased TU of nonfunctional isoforms of DNA repair genes was prevalent in breast and ovarian cancer with gHRD. Functional assays validated the association of aTU with impaired HR activity. Machine learning-based tHRD detection by the transcript usage (TU) pattern of key genes was superior to directly screening for gHRD or BRCA1/2 mutations in accurately predicting responses of cell lines and patients with cancer to PARP inhibitors and genotoxic drugs. This approach demonstrated the capability of tHRD status to reflect functional HR status, including in a cohort of olaparib-treated ovarian cancer with acquired platinum resistance. Diagnostic tests based on tHRD are expected to broaden the clinical utility of PARP inhibitors. SIGNIFICANCE: A novel but widespread transcriptional mechanism by which homologous recombination deficiency arises independently of BRCA1/2 mutations can be utilized as a companion diagnostic for PARP inhibitors.

Increased Cholesterol Biosynthesis Is a Key Characteristic of Breast Cancer Stem Cells Influencing Patient Outcome

Tumor eradication may be greatly improved by targeting cancer stem cells (CSCs), as they exhibit resistance to conventional therapy. To gain insight into the unique biology of CSCs, we developed patient-derived xenograft tumors (PDXs) from ER^- breast cancers from which we isolated mammospheres that are enriched for CSCs. Comparative global proteomic analysis was performed on patient tumor tissues and corresponding PDXs and mammospheres. Mammospheres exhibited increased expression of proteins associated with de novo cholesterol synthesis. The clinical relevance of increased cholesterol biosynthesis was verified in a large breast cancer cohort showing correlation with shorter relapse-free survival. RNAi and chemical inhibition of the cholesterol biosynthesis pathway reduced mammosphere formation, which could be rescued by a downstream metabolite. Our findings identify the cholesterol biosynthesis pathway as central for CSC propagation and a potential therapeutic target, as well as providing a mechanistic explanation for the therapeutic benefit of statins in breast cancer.

Computational inference of cancer-specific vulnerabilities in clinical samples

BACKGROUND

Systematic in vitro loss-of-function screens provide valuable resources that can facilitate the discovery of drugs targeting cancer vulnerabilities.

RESULTS

We develop a deep learning-based method to predict tumor-specific vulnerabilities in patient samples by leveraging a wealth of in vitro screening data. Acquired dependencies of tumors are inferred in cases in which one allele is disrupted by inactivating mutations or in association with oncogenic mutations. Nucleocytoplasmic transport by Ran GTPase is identified as a common vulnerability in Her2-positive breast cancers. Vulnerability to loss of Ku70/80 is predicted for tumors that are defective in homologous recombination and rely on nonhomologous end joining for DNA repair. Our experimental validation for Ran, Ku70/80, and a proteasome subunit using patient-derived cells shows that they can be targeted specifically in particular tumors that are predicted to be dependent on them.

CONCLUSION

This approach can be applied to facilitate the development of precision therapeutic targets for different tumors.

Inhibition of p90RSK activation sensitizes triple-negative breast cancer cells to cisplatin by inhibiting proliferation, migration and EMT

Cisplatin (Cis-DDP) is one of the most widely used anti-cancer drugs. It is applicable to many types of cancer, including lung, bladder, and breast cancer. However, its use is now limited because of drug resistance. p90 ribosomal S6 kinase (p90RSK) is one of the downstream effectors in the extracellular signal-regulated protein kinases 1 and 2 (ERK1/2) pathway and high expression of p90RSK is observed in human breast cancer tissues. Therefore, we investigated the role of p90RSK in the Cis-DDP resistance-related signaling pathway and epithelial-mesenchymal transition (EMT) in breast cancer cells. First, we discovered that MDA-MB-231 cells exhibited more Cis-DDP resistance than other breast cancer cells, including MCF-7 and BT549 cells. Cis-DDP increased p90RSK activation, whereas the inactivation of p90RSK using a small interfering RNA (siRNA) or dominant-negative kinase mutant plasmid overexpression significantly reduced Cis-DDP-induced cell proliferation and migration via the inhibition of matrix metallopeptidase (MMP)2 and MMP9 in MDA-MB-231 cells. In addition, p90RSK activation was involved in EMT via the upregulation of mRNA expression, including that of Snail, Twist, ZEB1, N-cadherin, and vimentin. We also investigated NF-κB, the upstream regulator of EMT markers, and discovered that Cis-DDP treatment led to NF-κB translocation in the nucleus as well as its promoter activity. Our results suggest that targeting p90RSK would be a good strategy to increase Cis-DDP sensitivity in triple-negative breast cancers.

Genetic heterogeneity and clonal evolution during metastasis in breast cancer patient-derived tumor xenograft models

Genetic heterogeneity within a tumor arises by clonal evolution, and patients with highly heterogeneous tumors are more likely to be resistant to therapy and have reduced survival. Clonal evolution also occurs when a subset of cells leave the primary tumor to form metastases, which leads to reduced genetic heterogeneity at the metastatic site. Although this process has been observed in human cancer, experimental models which recapitulate this process are lacking. Patient-derived tumor xenografts (PDX) have been shown to recapitulate the patient's original tumor's intra-tumor genetic heterogeneity, as well as its genomics and response to treatment, but whether they can be used to model clonal evolution in the metastatic process is currently unknown. Here, we address this question by following genetic changes in two breast cancer PDX models during metastasis. First, we discovered that mouse stroma can be a confounding factor in assessing intra-tumor heterogeneity by whole exome sequencing, thus we developed a new bioinformatic approach to correct for this. Finally, in a spontaneous, but not experimental (tail-vein) metastasis model we observed a loss of heterogeneity in PDX metastases compared to their orthotopic "primary" tumors, confirming that PDX models can faithfully mimic the clonal evolution process undergone in human patients during metastatic spreading.

Patient-Derived Xenograft Models of Breast Cancer and Their Application

Recently, patient-derived xenograft (PDX) models of many types of tumors including breast cancer have emerged as a powerful tool for predicting drug efficacy and for understanding tumor characteristics. PDXs are established by the direct transfer of human tumors into highly immunodeficient mice and then maintained by passaging from mouse to mouse. The ability of PDX models to maintain the original features of patient tumors and to reflect drug sensitivity has greatly improved both basic and clinical study outcomes. However, current PDX models cannot completely predict drug efficacy because they do not recapitulate the tumor microenvironment of origin, a failure which puts emphasis on the necessity for the development of the next generation PDX models. In this article, we summarize the advantages and limitations of current PDX models and discuss the future directions of this field.

LncRNA DANCR contributes to tumor progression via targetting miR-216a-5p in breast cancer: lncRNA DANCR contributes to tumor progression

Breast cancer, the most frequently occurring malignant tumor, has high mortality rate, especially triple-negative breast cancer (TNBC). LncRNA-differentiation antagonizing non-protein coding RNA (lncRNA DANCR) has been found that its aberrant expression was associated with tumor progression and it was promising to be a potential target for cancer therapy. The goal of the present study was to explore the biological effects and underlying mechanism of DANCR in breast cancer. Our results showed that DANCR was up-regulated in TNBC tissues and breast cancer cells compared with normal breast tissues and cells, and higher DANCR level suggested poorer prognosis, implying that it was promising to be a novel biomarker used for TNBC diagnosis and prognosis. To better research the functions and mechanism of DANCR on breast cancer cells, we selected two cell lines used for next study: one TNBC cell line–MDA-MB-231 and one ER-positive breast cancer cell line–MCF-7. Further study indicated that DANCR overexpression significantly promoted cell proliferation and invasion in vitro and contributed to tumor growth in vivo. To deeply understand its molecular mechanism, miRNA-216a-5p was identified as a target of DANCR by bioinformatic analysis. Experiments demonstrated that miRNA-216a-5p interacted with DANCR and its inhibitor could weaken the influences induced by DANCR knockdown for cancer cells, including cell proliferation and invasion, and the expression of Nanog, SOX2, and OCT4. Therefore, DANCR might act as a tumor promoter by targetting miRNA-216a-5p, which might provide a potential therapy target for breast cancer treatment.

[Establishment of A Patient-derived Xenotransplantation Animal Model for Small Cell Lung Cancer and Drug Resistance Model]

背景与目的

小细胞肺癌（small cell lung cancer, SCLC）是细胞分化程度低，恶性程度高，生长速度快，早期容易发生转移的恶性肿瘤。目前SCLC患者的临床治疗以化疗为主，但是在治疗6个月-9个月后极易发生获得性耐药而复发。因此，构建有效的临床前SCLC动物模型具有重要的临床价值。人源性肿瘤异种移植动物模型（patient-derived xenotransplantation, PDX）能够较好地保留原发肿瘤的特性，是比较理想的临床前动物模型。本研究旨在构建中国人来源的SCLC PDX动物模型，并诱导构建化疗耐药的PDX模型，为研究SCLC耐药机制及个体化治疗提供实验模型。

方法

取临床SCLC患者的新鲜手术切除标本或穿刺标本，移植至重度免疫缺陷小鼠NOD-PrkdcscidIL2rgtm1/Bcgen（B-NSGTM）皮下，HE染色及免疫组化对比移植肿瘤组织与患者肿瘤组织的病理学一致性。给予可稳定传代的每一代PDX模型小鼠腹腔注射8个周期的化疗药物（顺铂8 mg/kg+依托泊苷5mg/kg），定期监测荷瘤小鼠体质量和肿瘤体积，对长至1, 000 mm³大小的肿瘤进行传代移植。

结果

本研究收集并移植9例SCLC肿瘤标本，成功构建可多次传代SCLC PDX模型3例并成功诱导其耐药模型，模型较好地保留了原发肿瘤的特征。

结论

成功构建了SCLC PDX模型及其耐药模型，建模成功率为33%。为后续研究人的SCLC耐药机制、临床药物筛选以及个体化治疗提供了实验平台。

An accurate regression of developmental stages for breast cancer based on transcriptomic biomarkers

AIM

Breast cancers at different stages have tremendous differences on both phenotypic and molecular patterns. The developmental stage is an essential factor in the clinical decision of treatment plans, but was usually formulated as a classification problem, which ignored the consecutive relationships among them.

MATERIALS & METHODS

This study proposed a regression-based procedure to detect the stage biomarkers of breast cancers. Biomarkers were detected by the Lasso and Ridge algorithms.

RESULTS & CONCLUSION

A collaboration duet of Lasso and Ridge regression algorithms achieved the best performances, with classification accuracy (Acc) equal to 0.8294 and regression goodness-of-fit (R²) equal to 0.7810. The 265 biomarker genes were enriched with the signal peptide-based secretion function with the Bonferroni-corrected p-value equal to 6.9408e-3 and false discovery rate (FDR) equal to 1.1614e-2.