Stage-specific prognostic biomarkers in melanoma

Photoacoustic imaging for cutaneous melanoma assessment: a comprehensive review

Significance

Cutaneous melanoma (CM) has a high morbidity and mortality rate, but it can be cured if the primary lesion is detected and treated at an early stage. Imaging techniques such as photoacoustic (PA) imaging (PAI) have been studied and implemented to aid in the detection and diagnosis of CM.

Aim

Provide an overview of different PAI systems and applications for the study of CM, including the determination of tumor depth/thickness, cancer-related angiogenesis, metastases to lymph nodes, circulating tumor cells (CTCs), virtual histology, and studies using exogenous contrast agents.

Approach

A systematic review and classification of different PAI configurations was conducted based on their specific applications for melanoma detection. This review encompasses animal and preclinical studies, offering insights into the future potential of PAI in melanoma diagnosis in the clinic.

Results

PAI holds great clinical potential as a noninvasive technique for melanoma detection and disease management. PA microscopy has predominantly been used to image and study angiogenesis surrounding tumors and provide information on tumor characteristics. Additionally, PA tomography, with its increased penetration depth, has demonstrated its ability to assess melanoma thickness. Both modalities have shown promise in detecting metastases to lymph nodes and CTCs, and an all-optical implementation has been developed to perform virtual histology analyses. Animal and human studies have successfully shown the capability of PAI to detect, visualize, classify, and stage CM.

Conclusions

PAI is a promising technique for assessing the status of the skin without a surgical procedure. The capability of the modality to image microvasculature, visualize tumor boundaries, detect metastases in lymph nodes, perform fast and label-free histology, and identify CTCs could aid in the early diagnosis and classification of CM, including determination of metastatic status. In addition, it could be useful for monitoring treatment efficacy noninvasively.

Prognostic biomarkers of cutaneous melanoma

BACKGROUND/PURPOSE

Melanomas account for only approximately 4% of diagnosed skin cancers in the United States but are responsible for the majority of deaths caused by skin cancer. Both genetic factors and ultraviolet (UV) radiation exposure play a role in the development of melanoma. Although melanomas have a strong propensity to metastasize when diagnosed late, melanomas that are diagnosed and treated early pose a low mortality risk. In particular, the identification of patients with increased metastatic risk, who may benefit from early adjuvant therapies, is crucial, especially given the advent of new melanoma treatments. However, the accuracy of classic clinical and histological variables, including the Breslow thickness, presence of ulceration, and lymph node status, might not be sufficient to identify such individuals. Thus, there is a need for the development of additional prognostic melanoma biomarkers that can improve early attempts to stratify melanoma patients and reliably identify high-risk subgroups with the aim of providing effective personalized therapies.

METHODS

In our current work, we discuss and assess emerging primary melanoma tumor biomarkers and prognostic circulating biomarkers.

RESULTS

Several promising biomarkers show prognostic value (eg, exosomal MIA (ie, melanoma inhibitory activity), serum S100B, AMLo signatures, and mRNA signatures); however, the scarcity of reliable data precludes the use of these biomarkers in current clinical applications.

CONCLUSION

Further research is needed on several promising biomarkers for melanoma. Large-scale studies are warranted to facilitate the clinical translation of prognostic biomarker applications for melanoma in personalized medicine.

A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data

In high-throughput profiling studies, extensive efforts have been devoted to searching for the biomarkers associated with the development and progression of complex diseases. The heterogeneity of covariate effects associated with the outcomes across subjects has been noted in the literature. In this paper, we consider a scenario where the effects of covariates change smoothly across subjects, which are ordered by a known auxiliary variable. To this end, we develop a penalization-based approach, which applies a penalization technique to simultaneously select important covariates and estimate their unique effects on the outcome variables of each subject. We demonstrate that, under the appropriate conditions, our method shows selection and estimation consistency. Additional simulations demonstrate its superiority compared to several competing methods. Furthermore, applying the proposed approach to two The Cancer Genome Atlas datasets leads to better prediction performance and higher selection stability.

Current Trends in Circulating Biomarkers for Melanoma Detection

Melanomas have increased in global incidence and are the leading cause of skin cancer deaths. Whilst the majority of early-stage, non-metastatic melanomas can be cured with surgical excision alone, ~5% of patients with early melanomas will experience recurrence following a variable disease-free interval and progression to metastatic melanoma and ultimately death. This is likely because of primary tumor heterogeneity and progressive clonal divergency resulting in the growth of more aggressive tumor populations. Liquid biomarkers have the advantage of real-time, non-invasive longitudinal monitoring of tumor burden and heterogeneity over tissue markers. Currently, the only serological marker used in the staging and monitoring of melanoma is serum lactate dehydrogenase, which is not sufficiently specific or sensitive, and is not used routinely in all centers. An ideal melanoma biomarker would be used to identify patients who are at high-risk of primary melanoma, screen for relapse, detect early-stage melanoma, provide treatment outcomes to personalize systemic treatment, follow tumor heterogeneity, provide prognostic data before, during and after treatment, and monitor response to treatment. This review provides a summary of the current research in this field with a specific focus on circulating tumor cells, circulating tumor DNA, microRNA, and extracellular vesicles which may serve to suit these goals.

Treat Now or Treat Later: Comparative Effectiveness of Adjuvant Therapy in Resected Stage IIIA Melanoma

BACKGROUND

Adjuvant therapy for most sentinel-node-positive (stage IIIA) melanoma may have limited clinical benefit for older patients given the competing risk of non-cancer death. The objective of this study is to model the clinical effect and cost of adjuvant therapy in stage IIIA melanoma across age groups.

STUDY DESIGN

A Markov decision analysis model simulated the overall survival of patients with resected stage IIIA melanoma treated with adjuvant therapy vs observation. In the adjuvant approach, patients are modeled to receive adjuvant pembrolizumab (BRAF wild type) or dabrafenib/trametinib (BRAF mutant). In the observation approach, treatment is deferred until recurrence. Transition variables were derived from landmark randomized trials in adjuvant and salvage therapy. The model was analyzed for age groups spanning 40 to 89 years. The primary outcome was the number needed to treat (NNT) to prevent one melanoma-related death at 10 years. Cost per mortality avoided was estimated using Medicare reimbursement rates.

RESULTS

Projections for NNT among BRAF wild type patients increased by age from 14.71 (age 40 to 44) to 142.86 (age 85 to 89), with patients in cohorts over the age of 75 having an NNT over 25. The cost per mortality avoided ranged from $2.75 million (M) (age 40 to 44) to $27.57M (age 85 to 89). Corresponding values for BRAF mutant patients were as follows: NNT 18.18 to 333.33; cost per mortality avoided ranged from $2.75M to $54.70M.

CONCLUSION

Universal adjuvant therapy for stage IIIA melanoma is costly and provides limited clinical benefit in patients older than 75 years.

Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review

Over 100,000 people are diagnosed with cutaneous melanoma each year in the United States. Despite recent advancements in metastatic melanoma treatment, such as immunotherapy, there are still over 7000 melanoma-related deaths each year. Melanoma is a highly heterogenous disease, and many underlying genetic drivers have been identified since the introduction of next-generation sequencing. Despite clinical staging guidelines, the prognosis of metastatic melanoma is variable and difficult to predict. Bioinformatic and machine learning analyses relying on genetic, clinical, and histopathologic inputs have been increasingly used to risk stratify melanoma patients with high accuracy. This literature review summarizes the key genetic drivers of melanoma and recent applications of bioinformatic and machine learning models in the risk stratification of melanoma patients. A robustly validated risk stratification tool can potentially guide the physician management of melanoma patients and ultimately improve patient outcomes.

BRAF: A Two-Faced Janus

Gain-of-function of V-Raf Murine Sarcoma Viral Oncogene Homolog B (BRAF) is one of the most frequent oncogenic mutations in numerous cancers, including thyroid papillary carcinoma, melanoma, colon, and lung carcinomas, and to a lesser extent, ovarian and glioblastoma multiforme. This mutation aberrantly activates the mitogen-activated protein (MAP) kinase extracellular signal-regulated kinase (MEK)/extracellular signal-regulated kinase (ERK) signaling pathway, thereby eliciting metastatic processes. The relevance of BRAF mutations stems from its prognostic value and, equally important, from its relevant therapeutic utility as an actionable target for personalized treatment. Here, we discuss the double facets of BRAF. In particular, we argue the need to implement diagnostic molecular algorithms that are able to detect this biomarker in order to streamline and refine diagnostic and therapeutic decisions.

Six-gene-based prognostic model predicts overall survival in patients with uveal melanoma

BACKGROUND

Uveal melanoma (UM) is the most common primary intraocular tumor in adults, which has a high mortality rate and worse prognosis. Therefore, early potential molecular detection and prognostic evaluation seem more important for early diagnosis and treatment.

METHODS

Gene expression data were obtained from The Cancer Genome Atlas-Uveal melanomas database. Survival genes were identified by univariate analysis and were regarded to be associated with the overall survival of UM patients. Then, pathway enrichment analysis of these survival genes was performed. Robust likelihood-based survival model and multivariate survival analysis were conducted to identify more reliable genes and the prognostic signature for UM survival prediction. Two internal datasets and another two UM datasets from Gene Expression Omnibus (GEO) were used for the validation of prognostic signature.

RESULTS

Firstly, 2,010 survival genes were screened by univariate survival analysis. GO and KEGG analysis revealed that these genes were mainly involved in pathways such as mRNA processing, RNA splicing, spliceosome and ubiquitin mediated proteolysis. Secondly, a six-gene signature was identified by Robust likelihood-based survival model approach. The gene expression of the six genes can successfully divide UM samples into high- and low-risk groups and have strong survival prediction ability. What's more, the expression of six genes was compared in 80 healthy adipose tissue samples obtained from GTEx (Genotype-Tissue Expression) database and further validated in internal datasets and GEO datasets, which also can predict UM patient survival.

CONCLUSIONS

The six genes (SH2D3A, TMEM201, LZTS1, CREG1, NIPA1 and HIST1H4E) model might play a vital role in prognosis of UM, which should be helpful for further insight into the treatment of uveal melanoma.

Conditional survival analysis of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide with an approximate 5-year survival of greater than 50% in patients after surgical resection. Survival estimates have limited utility for patients who have survived several years after initial treatment. We analyzed how conditional survival (CS) after curative-intent surgery for HCC predicts survival estimates over time.

METHODS

NCDB (2004-2014) was queried for patients undergoing definitive surgical resection for HCC. Cumulative overall survival (OS) was calculated using the Kaplan-Meier method, and CS at x years after diagnosis was calculated as CS1  = OS (X+5) /OS(X) .

RESULTS

The final analysis encompassed 11 357 patients. Age, negative margin status, grade severity and radiation before surgery were statistically significant predictors of cumulative overall conditional survival (P ≤ .0001). Overall unconditional 5-year survival was 65.7%, but CS estimates were higher. A patient who has already survived 3 years has an additional 2-year, or 5-year CS, estimate of 86.96%.

CONCLUSION

Survival estimates following hepatic resection in HCC patients change according to survival time accrued since surgery. CS estimates are improved relative to unconditional OS. The impact of different variables influencing OS is likewise nonlinear over the course of time after surgery.

BRAF mutational status as a prognostic marker for survival in malignant melanoma: a systematic review and meta-analysis

Background: The analysis of the BRAF mutational status has been established as a standard procedure during diagnosis of advanced malignant melanoma due to the fact that BRAF inhibitors constitute a cornerstone in the treatment of metastatic disease. However, the general impact of BRAF mutational status on survival remains unclear. Our study aimed to assess the underlying prognostic significance of BRAF mutant versus wild type (WT) malignant melanoma on overall survival (OS), disease-free survival (DFS) and progression-free survival (PFS).Material and methods: A systematic literature search in EMBASE, Medline and Cochrane CENTRAL was performed. Studies were included if they reported survival outcomes for BRAF mutant versus WT patients as hazard ratios (HR) or in Kaplan-Meier (KM) curves. Random-effects meta-analysis models were used to pool HRs across the studies.Results: Data from 52 studies, representing 7519 patients, were pooled for analysis of OS. The presence of a BRAF mutation was statistically significantly associated with a reduced OS (HR [95% confidence interval (CI)]: 1.23 [1.09-1.38]), however, with substantial heterogeneity between the studies (I²: 58.0%). Meta-regression and sensitivity analyses showed that age, sex and BRAF mutation testing method did not have a significant effect on the OS HR. BRAF mutant melanoma showed comparable effect on DFS to non-BRAF mutant melanoma in stage I-III melanoma (combined HR: 1.16, 95% CI: 0.92-1.46), and on PFS in stage III-IV (HR: 0.98 (95% CI: 0.68-1.40)).Conclusion: Although there was substantial heterogeneity between the studies, the overall results demonstrated a poorer prognosis and OS in patients harbouring BRAF mutations. Future studies should take this into account when evaluating epidemiological data and treatment effects of new interventions in patients with malignant melanoma.

Penalized integrative semiparametric interaction analysis for multiple genetic datasets

In this article, we consider a semiparametric additive partially linear interaction model for the integrative analysis of multiple genetic datasets. The goals are to identify important genetic predictors and gene-gene interactions and to estimate the nonparametric functions that describe the environmental effects at the same time. To find the similarities and differences of the genetic effects across different datasets, we impose a group structure on the regression coefficients matrix under the homogeneity assumption, ie, models for different datasets share the same sparsity structure, but the coefficients may differ across datasets. We develop an iterative approach to estimate the parameters of main effects, interactions and nonparametric functions, where a reparametrization of interaction parameters is implemented to meet the strong hierarchy assumption. We demonstrate the advantages of the proposed method in identification, estimation, and prediction in a series of numerical studies. We also apply the proposed method to the Skin Cutaneous Melanoma data and the lung cancer data from the Cancer Genome Atlas.

Identification of cancer omics commonality and difference via community fusion

The analysis of cancer omics data is a "classic" problem; however, it still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datasets, with each dataset on one type/subtype of "related" cancers. A Community Fusion (CoFu) approach is developed, which conducts marker selection and model building using a novel penalization technique, informatively accommodates the network community structure of omics measurements, and automatically identifies the commonality and difference of cancer omics markers. Simulation demonstrates its superiority over direct competitors. The analysis of TCGA lung cancer and melanoma data leads to interesting findings.

A 14-Protein Signature for Rapid Identification of Poor Prognosis Stage III Metastatic Melanoma

PURPOSE

To validate differences in protein levels between good and poor prognosis American Joint Committee on Cancer (AJCC) stage III melanoma patients and compile a protein panel to stratify patient risk.

EXPERIMENTAL DESIGN

Protein extracts from melanoma metastases within lymph nodes in patients with stage III disease with good (n = 16, >4 years survival) and poor survival (n = 14, <2 years survival) were analyzed by selected reaction monitoring (SRM). Diagonal Linear Discriminant Analysis (DLDA) was performed to generate a protein biomarker panel.

RESULTS

SRM analysis identified ten proteins that were differentially abundant between good and poor prognosis stage III melanoma patients. The ten differential proteins were combined with 22 proteins identified in our previous work. A panel of 14 proteins was selected by DLDA that was able to accurately classify patients into prognostic groups based on levels of these proteins.

CONCLUSIONS AND CLINICAL RELEVANCE

The ten differential proteins identified by SRM have biological significance in cancer progression. The final signature of 14 proteins identified by SRM could be used to identify AJCC stage III melanoma patients likely to have poor outcomes who may benefit from adjuvant systemic therapy.

MMP-9 overexpression is associated with intragenic hypermethylation of MMP9 gene in melanoma

Tumor spreading is associated with the degradation of extracellular matrix proteins, mediated by the overexpression of matrix metalloproteinase 9 (MMP-9). Although, such overexpression was linked to epigenetic promoter methylation, the role of intragenic methylation was not clarified yet. Melanoma was used as tumor model to investigate the relationship between the DNA intragenic methylation of MMP9 gene and MMP-9 overexpression at transcriptional and protein levels. Computational analysis revealed DNA hypermethylation within the intragenic CpG-2 region of MMP9 gene in melanoma samples with high MMP-9 transcript levels. In vitro validation showed that CpG-2 hotspot region was hypermethylated in the A375 melanoma cell line with highest mRNA and protein levels of MMP-9, while low methylation levels were observed in the MEWO cell line where MMP-9 was undetectable. Concordant results were demonstrated in both A2058 and M14 cell lines. This correlation may give further insights on the role of MMP-9 upregulation in melanoma.

Recent advances in melanoma research via "omics" platforms

Melanoma has a high mortality rate and metastatic melanoma is highly resistant to conventional therapies. "Omics" fields such as proteomics and microRNA and exosome studies have provided new knowledge to complement the information generated by genomic studies. This work aimed to review the current status of biomarker discovery for melanoma through multi-"omics" platforms. A few sets of novel microRNAs and proteins are described, some of them with important implications in suppressing melanoma at different stages. Upregulation of genes involved in angiogenesis, immunosuppressive factors, modification of stroma, capture of melanoma cells in lymph nodes and factors responsible for tumour cell recruitment have been identified in exosomes, among molecules with other functions. A remarkable series of proteins involved in epithelial-mesenchymal/mesenchymal-epithelial transitions, inflammation, motility, proliferation and progression processes, centrosome amplification, aneuploidy, inhibition of CD8+ effector T-cells, and metastasis in general were identified. Genomic and protein-protein interactions or metabolome levels were not analysed. Proteomics tools such as Orbitrap shotgun mass spectrometry or deep mining proteomic analysis utilizing high-resolution reversed phase nanoseparation in combination with mass spectrometry are also discussed. The application of these tools together with bioinformatics approaches applied to the clinical setting will enable the implementation of personalized medicine in the near future.

Up-regulation of SERPINA3 correlates with high mortality of melanoma patients and increased migration and invasion of cancer cells

Serpin Peptidase Inhibitor, clade A member 3 (SERPINA3) was found to be abnormally overexpressed in a subset of melanoma tissue biopsies. High SERPINA3 expression was also associated with poor patient survival. In this study, we set out to test SERPINA3 protein's prognostic potential with a larger-sized and independent patient cohort, and to explore SERPINA3's function in melanoma cells. Tissue microarray-based immunohistochemistry analysis showed a significant increase in SERPINA3 expression in invasive and metastatic melanomas compared to normal nevi and melanoma-in-situ (P < 0.001, Chi-square test). In melanoma patients, high SERPINA3 expression was strongly associated with worse overall and disease specific survival at 5 years. Multivariate Cox regression analysis showed that SERPINA3 expression is an independent prognostic factor to predict melanoma patient clinical outcome. When SERPINA3 expression was selectively silenced using small interfering RNA molecules (siRNA) in cultured melanoma cell lines, cell migration and matrix invasion was significantly decreased, but no change in cell proliferation was observed.This study confirms the prognostic potential of SERPINA3 expression in human cutaneous melanoma and reveals the pro-migration and pro-invasion functions of this protein on melanoma cells.

p27Kip1 promotes invadopodia turnover and invasion through the regulation of the PAK1/Cortactin pathway

p27^Kip1 (p27) is a cyclin-CDK inhibitor and negative regulator of cell proliferation. p27 also controls other cellular processes including migration and cytoplasmic p27 can act as an oncogene. Furthermore, cytoplasmic p27 promotes invasion and metastasis, in part by promoting epithelial to mesenchymal transition. Herein, we find that p27 promotes cell invasion by binding to and regulating the activity of Cortactin, a critical regulator of invadopodia formation. p27 localizes to invadopodia and limits their number and activity. p27 promotes the interaction of Cortactin with PAK1. In turn, PAK1 promotes invadopodia turnover by phosphorylating Cortactin, and expression of Cortactin mutants for PAK-targeted sites abolishes p27’s effect on invadopodia dynamics. Thus, in absence of p27, cells exhibit increased invadopodia stability due to impaired PAK1-Cortactin interaction, but their invasive capacity is reduced compared to wild-type cells. Overall, we find that p27 directly promotes cell invasion by facilitating invadopodia turnover via the Rac1/PAK1/Cortactin pathway.

DOI:http://dx.doi.org/10.7554/eLife.22207.001

When animals develop from embryos to adults, or try to heal wounds later in life, their cells have to move. Moving means that the cells must invade into their surroundings, a dense network of proteins called the extracellular matrix. The cell first attaches to the extracellular matrix; degrades it; and then moves into the newly opened space. Cells have developed specialized structures called invadosomes to enable all these steps. Invadosomes are never static, they first assemble where cells interact with extracellular matrix, they then release proteins that loosen the matrix, and finally disassemble again to allow cells to move. Invadosomes in cancer cells often become overactive, and can allow the tumor cells to spread throughout the body.

A lot of different proteins are involved in controlling how and when cells move. p27 is a well-known protein usually found in a cell’s nucleus along with the cell’s DNA. Inside the nucleus, p27 suppresses tumor growth by stopping cells from dividing. However, often in cancer cells p27 moves outside of the cell’s nucleus where it contributes to cell movement via an unknown mechanism.

To answer how p27 controls cell invasion, Jeannot et al. used a biochemical technique to uncover which proteins p27 binds to when it is outside of the nucleus. One of its interaction partners was called Cortactin. This protein is known to be an important building block of invadosomes, and is involved in both the assembly and disassembly of these structures. In further experiments, Jeannot studied mouse cells with or without p27 and human cancer cells that can be grown in the laboratory. The experiments revealed that p27 promotes an enzyme called PAK1 to also bind to Cortactin. PAK1 then modified Cortactin, causing whole invadosomes to disassemble, which in turn allowed cells to de-attach from the matrix and move forward. In contrast, cells lacking p27 had more stable invadosomes, attached more strongly to the matrix and were better at degrading it, but could not invade as well as cells with p27.

Overall these experiments showed a new way that p27 promotes cell invasion. The next steps will include finding out exactly how the modification of Cortactin causes the invadosomes to disassemble. Furthermore, it will be important to study whether forcing p27 back into the nucleus can reduce the spread of cancer cells in the body.

DOI:http://dx.doi.org/10.7554/eLife.22207.002

Loss of p27Kip¹ promotes metaplasia in the pancreas via the regulation of Sox9 expression

p27^Kip1 (p27) is a negative regulator of proliferation and a tumor suppressor via the inhibition of cyclin-CDK activity in the nucleus. p27 is also involved in the regulation of other cellular processes, including transcription by acting as a transcriptional co-repressor. Loss of p27 expression is frequently observed in pancreatic adenocarcinomas in human and is associated with decreased patient survival. Similarly, in a mouse model of K-Ras-driven pancreatic cancer, loss of p27 accelerates tumor development and shortens survival, suggesting an important role for p27 in pancreatic tumorigenesis. Here, we sought to determine how p27 might contribute to early events leading to tumor development in the pancreas. We found that K-Ras activation in the pancreas causes p27 mislocalization at pre-neoplastic stages. Moreover, loss of p27 or expression of a mutant p27 that does not bind cyclin-CDKs causes the mislocalization of several acinar polarity markers associated with metaplasia and induces the nuclear expression of Sox9 and Pdx1 two transcription factors involved in acinar-to-ductal metaplasia. Finally, we found that p27 directly represses transcription of Sox9, but not that of Pdx1. Thus, our results suggest that K-Ras activation, the earliest known event in pancreatic carcinogenesis, may cause loss of nuclear p27 expression which results in derepression of Sox9, triggering reprogrammation of acinar cells and metaplasia.

Human hemokinin-1 promotes migration of melanoma cells and increases MMP-2 and MT1-MMP expression by activating tumor cell NK1 receptors

Receptors and their regulatory peptides are aberrantly expressed in tumors, suggesting a potential tumor therapy target. Human hemokinin-1 (hHK-1) is a tachykinin peptide ligand of the neurokinin-1 (NK1) receptor which is overexpressed in melanoma and other tumor tissues. Here, we investigated the role of hHK-1 and the NK1 receptor in melanoma cell migration. NK1 receptor expression was associated with melanoma metastatic potential. Treatment with hHK-1 significantly enhanced A375 and B16F10 melanoma cell migration and an NK1 receptor antagonist L732138 blocked this effect. MMP-2 and MT1-MMP expression were up-regulated in hHK-1-treated melanoma cells and cell signaling data suggested that hHK-1 induced phosphorylation of ERK1/2, JNK and p38 by way of PKC or PKA. Kinase activation led to increased MMP-2 and MT1-MMP expression and melanoma cell migration induced by hHK-1. Thus, hHK-1 and the NK1 receptor are critical to melanoma cell migration and each may be a promising chemotherapeutic target.

Conditional disease-free survival after liver transplantation for hepatocellular carcinoma: A two-center experience

Traditionally, survival estimates following liver transplantation (LT) of hepatocellular carcinoma (HCC) patients were calculated as survival from the surgery date, but future survival probabilities can change over time and conditional disease-free survival (CDFS) may provide patients and clinicians with more accurate prognostic information. This study aimed to assess CDFS in HCC patients after LT.Three hundred eighty-four HCC patients who underwent LT were included. Disease-free survival (DFS) was calculated using the Kaplan-Meier analysis. The 3-year CDFS, which represents the probability of remaining disease free for an additional 3 years, was calculated.1-, 3-, and 5-year DFS rates after LT were 69.9%, 45.8%, and 39.0 %, respectively. Based on the concept of CDFS, the probability of surviving an additional 3 years given that the patient was disease free at 1 year, 3 years, and 5 years were 58.4%, 76.9%, and 83.1%, respectively. Multivariate analysis indicated that larger tumor size (hazard ratio [HR], 1.509; 95% CI, 1.146-1.985; P = 0.003) was associated with poorer DFS. Patients with worse prognostic features at baseline demonstrated the greater increase in CDFS over time.Survival estimates following liver transplantation of HCC patients change according to survival time accrued since surgery. CDFS estimates improved dramatically over time especially among patients with worse prognostic features at the time of surgery. CDFS may be a useful tool in counseling patients with HCC, as it is a more accurate assessment of future survival for those patients who have already survived a certain amount of time.

Rewiring of the apoptotic TGF-β-SMAD/NFκB pathway through an oncogenic function of p27 in human papillary thyroid cancer

Papillary thyroid carcinoma (PTC), the most frequent thyroid cancer, is characterized by low proliferation but no apoptosis, presenting frequent lymph-node metastasis. Papillary thyroid carcinoma overexpress transforming growth factor-beta (TGF-β). In human cells, TGF-β has two opposing actions: antitumoral through pro-apoptotic and cytostatic activities, and pro-tumoral promoting growth and metastasis. The switch converting TGF-β from a tumor-suppressor to tumor-promoter has not been identified. In the current study, we have quantified a parallel upregulation of TGF-β and nuclear p27, a CDK2 inhibitor, in samples from PTC. We established primary cultures from follicular epithelium in human homeostatic conditions (h7H medium). TGF-β-dependent cytostasis occurred in normal and cancer cells through p15/CDKN2B induction. However, TGF-β induced apoptosis in normal and benign but not in carcinoma cultures. In normal thyroid cells, TGF-β/SMAD repressed the p27/CDKN1B gene, activating CDK2-dependent SMAD3 phosphorylation to induce p50 NFκB-dependent BAX upregulation and apoptosis. In thyroid cancer cells, oncogene activation prevented TGF-β/SMAD-dependent p27 repression, and CDK2/SMAD3 phosphorylation, leading to p65 NFκB upregulation which repressed BAX, induced cyclin D1 and promoted TGF-β-dependent growth. In PTC samples from patients, upregulation of TGF-β, p27, p65 and cyclin D1 mRNA were significantly correlated, while the expression of the isoform BAX-β, exclusively transcribed in apoptotic cells, was negatively correlated. Additionally, combined ERK and p65 NFκB inhibitors reduced p27 expression and potentiated apoptosis in thyroid cancer cells while not affecting survival in normal thyroid cells. Our results therefore suggest that the oncoprotein p27 reorganizes the effects of TGF-β in thyroid cancer, explaining the slow proliferation but lack of apoptosis and metastatic behavior of PTC.

Vemurafenib resistance increases melanoma invasiveness and modulates the tumor microenvironment by MMP-2 upregulation

The BRAF(V600E) mutation confers constitutive kinase activity and accounts for >90% of BRAF mutations in melanoma. This genetic alteration is a current therapeutic target; however, the antitumorigenic effects of the BRAF(V600E) inhibitor vemurafenib are short-lived and the majority of patients present tumor relapse in a short period after treatment. Characterization of vemurafenib resistance has been essential to the efficacy of next generation therapeutic strategies. Herein, we found that acute BRAF inhibition induced a decrease in active MMP-2, MT1-MMP and MMP-9, but did not modulate the metalloproteinase inhibitors TIMP-2 or RECK in naïve melanoma cells. In vemurafenib-resistant melanoma cells, we observed a lower growth rate and an increase in EGFR phosphorylation followed by the recovery of active MMP-2 expression, a mediator of cancer metastasis. Furthermore, we found a different profile of MMP inhibitor expression, characterized by TIMP-2 downregulation and RECK upregulation. In a 3D spheroid model, the invasion index of vemurafenib-resistant melanoma cells was more evident than in its non-resistant counterpart. We confirmed this pattern in a matrigel invasion assay and demonstrated that use of a matrix metalloproteinase inhibitor reduced the invasion of vemurafenib resistant melanoma cells but not drug naïve cells. Moreover, we did not observe a delimited group of cells invading the dermis in vemurafenib-resistant melanoma cells present in a reconstructed skin model. The same MMP-2 and RECK upregulation profile was found in this 3D skin model containing vemurafenib-resistant melanoma cells. Acute vemurafenib treatment induces the disorganization of collagen fibers and consequently, extracellular matrix remodeling, with this pattern observed even after the acquisition of resistance. Altogether, our data suggest that resistance to vemurafenib induces significant changes in the tumor microenvironment mainly by MMP-2 upregulation, with a corresponding increase in cell invasiveness.

Prognostic and predictive biomarkers in melanoma: an update

Malignant melanoma is one of the most aggressive cancers. Several new therapeutic strategies that focus on immuno- and/or targeted therapy have been developed, which have entered clinical trials or already been approved. This review provides an update on prognostic and predictive biomarkers in melanoma that may be used to improve the clinical management of patients. Prognostic markers include conventional histopathological characteristics, chromosomal aberrations, gene expression patterns and miRNA profiles. There is a trend towards multi-marker assays and whole-genome molecular screening methods to determine the prognosis of individual patients. Predictive biomarkers, including targeted components of signal transduction, developmental or transcriptional pathways, can be used to determine patient response towards a particular treatment or combination thereof. The rapid evolution of sequencing technologies and multi-marker screening will change the spectrum of patients who become candidates for therapeutic agents, and in addition create new ethical and regulatory challenges.