Integrating RAS status into prognostic signatures for adenocarcinomas of the lung

Synergistic Inhibition of Drug-Resistant Colon Cancer Growth with PI3K/mTOR Dual Inhibitor BEZ235 and Nano-Emulsioned Paclitaxel via Reducing Multidrug Resistance and Promoting Apoptosis

BACKGROUND

Colon cancer is a top lethal cancer in man and women worldwide and drug resistance is the major cause of cancer-related death. Combinational therapy and drug delivery with nanoparticles have been shown to effectively overcome drug resistance in many cancers. We previously reported that nanoemulsion (NE) loaded paclitaxel (PTX) and BEZ235 could synergistically inhibit colon cancer cell growth.

PURPOSE

To investigate whether NE loaded PTX and BEZ235 can overcome drug resistance and synergistically inhibit drug-resistant colon cancer cell growth in vitro and in vivo.

METHODS

The in vitro treatment effect on cell viability was assayed using CCK8 kit, cell morphological change was detected by β-tubulin immunofluorescence staining, drug resistance-related proteins were analyzed by Western blotting, and in vivo tumor growth test was performed in nude mice xeno-transplanted with 2 drug-resistant colon cancer cell lines HCT116-LOHP and HT29-DDP.

RESULTS

Both cell lines were sensitive to PTX but relatively insensitive to BEZ235. PTX combined with BEZ235 synergistically inhibited the proliferation of both cell lines. Nanoemulsion loaded PTX (NE-PTX) reduced the IC50 of PTX to approximately 2/5 of free PTX, indicating a high inhibitory efficacy of NE-PTX. When NE-PTX combined with a low concentration of BEZ235 (50 nM), the IC50 was decreased to approximately 2/3 of free PTX. Moreover, NE-PTX+BEZ235 treatment increased apoptosis, decreased Pgp and ABCC1 expression, and reduced tumor weights compared to the single drug treatment and the control group. These results suggest that nanoemulsion loaded PTX+BEZ235 can overcome drug resistance and improve the inhibitory effect on cancer cell proliferation and tumor growth.

CONCLUSION

Our study thus provides a possible new approach to treat colon cancer patients with drug resistance.

The Mir181ab1 cluster promotes KRAS-driven oncogenesis and progression in lung and pancreas

Few therapies are currently available for patients with KRAS-driven cancers, highlighting the need to identify new molecular targets that modulate central downstream effector pathways. Here we found that the microRNA (miRNA) cluster including miR181ab1 is a key modulator of KRAS-driven oncogenesis. Ablation of Mir181ab1 in genetically engineered mouse models of Kras-driven lung and pancreatic cancer was deleterious to tumor initiation and progression. Expression of both resident miRNAs in the Mir181ab1 cluster, miR181a1 and miR181b1, was necessary to rescue the Mir181ab1-loss phenotype, underscoring their nonredundant role. In human cancer cells, depletion of miR181ab1 impaired proliferation and 3D growth, whereas overexpression provided a proliferative advantage. Lastly, we unveiled miR181ab1-regulated genes responsible for this phenotype. These studies identified what we believe to be a previously unknown role for miR181ab1 as a potential therapeutic target in 2 highly aggressive and difficult to treat KRAS-mutated cancers.

Identification of a prognostic 28-gene expression signature for gastric cancer with lymphatic metastasis

Gastric cancer (GC) patients have high mortality due to late-stage diagnosis, which is closely associated with lymph node metastasis. Exploring the molecular mechanisms of lymphatic metastasis may inform the research into early diagnostics of GC. In the present study, we obtained RNA-Seq data from The Cancer Genome Altas and used Limma package to identify differentially expressed genes (DEGs) between lymphatic metastases and non-lymphatic metastases in GC tissues. Then, we used an elastic net-regularized COX proportional hazard model for gene selection from the DEGs and constructed a regression model composed of 28-gene signatures. Furthermore, we assessed the prognostic performance of the 28-gene signature by analyzing the receive operating characteristic curves. In addition, we selected the gene PELI2 amongst 28 genes and assessed the roles of this gene in GC cells. The good prognostic performance of the 28-gene signature was confirmed in the testing set, which was also validated by GSE66229 dataset. In addition, the biological experiments showed that PELI2 could promote the growth and metastasis of GC cells by regulating vascular endothelial growth factor C. Our study indicates that the identified 28-gene signature could be considered as a sensitive predictive tool for lymphatic metastasis in GC.

An 8-gene signature for prediction of prognosis and chemoresponse in non-small cell lung cancer

Identification of a potential gene signature for improved diagnosis in non-small cell lung cancer (NSCLC) patient is necessary. Here, we aim to establish and validate the prognostic efficacy of a gene set that can predict prognosis and benefits of adjuvant chemotherapy (ACT) in NSCLC patients from various ethnicities. An 8-gene signature was calculated from the gene expression of 181 patients using univariate Cox proportional hazard regression analysis. The prognostic value of the signature was robustly validated in 1,477 patients from five microarray independent data sets and one RNA-seq data set. The 8-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses [hazard ratio (HR): 2.84, 95% confidence interval CI (1.74-4.65), p=3.06e-05, [HR] 2.62, 95% CI (1.51-4.53), p=0.001], respectively. Subset analysis demonstrated that the 8-gene signature could identify high-risk patients in stage II-III with improved survival from ACT [(HR) 1.47, 95% CI (1.01-2.14), p=0.044]. The 8-gene signature also stratified risk groups in EGFR-mutated and wild-type patients. In conclusion, the 8-gene signature is a strong and independent predictor that can significantly stratify patients into low- and high-risk groups. Our gene signature also has the potential to predict patients in stage II-III that are likely to benefit from ACT.

Prognostic and predictive biomarkers post curative intent therapy

Large-scale screening trials have demonstrated that early diagnosis of lung cancer results in a significant reduction in lung cancer mortality. Despite improvements in detecting more lung cancers at early stages, the 5-year survival rates of lung cancers diagnosed before widespread disease is only 30-50%. High rates of recurrence, despite early diagnosis, suggest the need to improve treatment strategies based on the likelihood of recurrence in patient subsets, as well as explore the role of predictive markers for therapy selection in the adjuvant setting. In the era of personalized medicine, there have been a wide array of molecular alterations and signatures studied for their potential prognostic and predictive utility, however most have failed to translate into clinical tools. This review will discuss progress made in clinical management of lung cancer, and recent progress in the development of patient selection tools for the refinement of early stage lung cancers.

How to Target Activated Ras Proteins: Direct Inhibition vs. Induced Mislocalization

Oncogenic Ras proteins are a driving force in a significant set of human cancers and wildtype, unmutated Ras proteins likely contribute to the malignant phenotype of many more. The overall challenge of targeting activated Ras proteins has great promise to treat cancer, but this goal has yet to be achieved. Significant efforts and resources have been committed to inhibiting Ras, but these energies have so far made little impact in the clinic. Direct attempts to target activated Ras proteins have faced many obstacles, including the fundamental nature of the gain-of-function oncogenic activity being produced by a loss-of-function at the biochemical level. Nevertheless, there has been very promising recent pre-clinical progress. The major strategy that has so far reached the clinic aimed to inhibit activated Ras indirectly through blocking its post-translational modification and inducing its mislocalization. While these efforts to indirectly target Ras through inhibition of farnesyl transferase (FTase) were rationally designed, this strategy suffered from insufficient attention to the distinctions between the isoforms of Ras. This led to subsequent failures in large-scale clinical trials targeting K-Ras driven lung, colon, and pancreatic cancers. Despite these setbacks, efforts to indirectly target activated Ras through inducing its mislocalization have persisted. It is plausible that FTase inhibitors may still have some utility in the clinic, perhaps in combination with statins or other agents. Alternative approaches for inducing mislocalization of Ras through disruption of its palmitoylation cycle or interaction with chaperone proteins are in early stages of development.

Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma

Background

Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.

Methods

Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n = 255) and validation (n = 157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided.

Results

In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00x10^-4, including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n = 67; HR = 2.05, 95% CI = 1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR = 3.05, 95% CI = 2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR = 2.78, 95% CI = 1.91 to 11.13), EGFR wild-type (HR = 3.01, 95% CI = 1.73 to 14.98), and EGFR mutant (HR = 8.99, 95% CI = 62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures.

Conclusions

Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.

KRAS-driven lung adenocarcinoma: combined DDR1/Notch inhibition as an effective therapy

Understanding the early evolution of cancer heterogeneity during the initial steps of tumorigenesis can uncover vulnerabilities of cancer cells that may be masked at later stages. We describe a comprehensive approach employing gene expression analysis in early lesions to identify novel therapeutic targets and the use of mouse models to test synthetic lethal drug combinations to treat human Kirsten rat sarcoma viral oncogene homologue (KRAS)-driven lung adenocarcinoma.

Tumor necrosis factor links chronic obstructive pulmonary disease and K-ras mutant lung cancer through induction of an immunosuppressive pro-tumor microenvironment

Tumor necrosis factor (TNF) is known as an important regulator of tumor microenvironment and inflammation. TNF levels are markedly elevated in the bronchoalveolar lavage fluid (BALF) of patients with chronic obstructive pulmonary disease (COPD), which is an independent risk factor for lung cancer. We have previously shown that COPD-like airway inflammation promotes lung cancer in a K-ras mutant mouse model (CC-LR mouse). This was associated with a significant increase of neutrophils in BALF, accompanied by a marked increase in TNF level, suggesting a link between COPD, TNF, and lung cancer promotion. Therefore, we first overexpressed TNF in the airway epithelium of CC-LR mice, which promoted lung cancer by ∼2-fold. This was associated with increased numbers of Ki67 and CD31 positive cells in lung tumors of CC-LR/TNF-Tg mice. We also found a robust increase in NF-κB activation, and numbers of neutrophils and myeloid-derived suppressor cells (MDSCs) in lung. Accordingly, we depleted MDSCs in CC-LR/TNF-Tg mice, which lead to significant tumor suppression emphasizing on the role of TNF-induced MDSCs in K-ras induced lung tumorigenesis. Finally, we targeted TNF expression by crossing CC-LR mice with TNF knock-out mice (CC-LR/TNF-KO), which resulted in a significant decrease in lung tumor burden in the absence or presence of COPD-like airway inflammation. Interestingly, there were less MDSCs and lower Ki67 and CD31 expression in the lung of the CC-LR/TNF-KO mice. We conclude that TNF links COPD to lung cancer promotion by induction of an immunosuppressive MDSC response, and subsequent amplification of proliferation and angiogenesis in tumors.

The path to routine use of genomic biomarkers in the cancer clinic

It has been almost 15 years since the first microarray-based studies creating multigene biomarkers to subtype and predict survival of cancer patients. This Perspective looks at why only a handful of genomic biomarkers have reached clinical application and what advances are needed over the next 15 years to grow this number. I discuss challenges in creating biomarkers and reproducing them at the genomic and computational levels, including the problem of spatio-genomic heterogeneity in an individual cancer. I then outline the challenges in translating newly discovered genome-wide or regional events, like trinucleotide mutation signatures, kataegis, and chromothripsis, into biomarkers, as well as the importance of incorporating prior biological knowledge. Lastly, I outline the practical problems of pharmaco-economics and adoption: Are new biomarkers viewed as economically rational by potential funders? And if they are, how can their results be communicated effectively to patients and their clinicians? Genomic-based diagnostics have immense potential for transforming the management of cancer. The next 15 years will see a surge of research into the topics here that, when combined with a stream of new targeted therapies being developed, will personalize the cancer clinic.