Molecular Signature Predictive of Long-Term Liver Fibrosis Progression to Inform Antifibrotic Drug Development.

BACKGROUND & AIMS: There is a major unmet need to assess the prognostic impact of antifibrotics in clinical trials because of the slow rate of liver fibrosis progression. We aimed to develop a surrogate biomarker to predict future fibrosis progression. METHODS: A fibrosis progression signature (FPS) was defined to predict fibrosis progression within 5 years in patients with hepatitis C virus and nonalcoholic fatty liver disease (NAFLD) with no to minimal fibrosis at baseline (n = 421) and was validated in an independent NAFLD cohort (n = 78). The FPS was used to assess response to 13 candidate antifibrotics in organotypic ex vivo cultures of clinical fibrotic liver tissues (n = 78) and cenicriviroc in patients with nonalcoholic steatohepatitis enrolled in a clinical trial (n = 19, NCT02217475). A serum protein-based surrogate FPS was developed and tested in a cohort of compensated cirrhosis patients (n = 122). RESULTS: A 20-gene FPS was defined and validated in an independent NAFLD cohort (adjusted odds ratio, 10.93; area under the receiver operating characteristic curve, 0.86). Among computationally inferred fibrosis-driving FPS genes, BCL2 was confirmed as a potential pharmacologic target using clinical liver tissues. Systematic ex vivo evaluation of 13 candidate antifibrotics identified rational combination therapies based on epigallocatechin gallate, which were validated for enhanced antifibrotic effect in ex vivo culture of clinical liver tissues. In patients with nonalcoholic steatohepatitis treated with cenicriviroc, FPS modulation was associated with 1-year fibrosis improvement accompanied by suppression of the E2F pathway. Induction of the PPARα pathway was absent in patients without fibrosis improvement, suggesting a benefit of combining PPARα agonism to improve the antifibrotic efficacy of cenicriviroc. A 7-protein serum protein-based surrogate FPS was associated with the development of decompensation in cirrhosis patients. CONCLUSION: The FPS predicts long-term fibrosis progression in an etiology-agnostic manner, which can inform antifibrotic drug development.

How are we evaluating the cost-effectiveness of companion biomarkers for targeted cancer therapies? A systematic review.

BACKGROUND: Despite the increasing economic assessment of biomarker-guided therapies, no clear agreement exists whether existing methods are sufficient or whether different methods might produce different cost-effectiveness results. This study aims to examine current practices of modeling companion biomarkers when assessing the cost-effectiveness of targeted cancer therapies. It investigates the current methods in modeling the characteristics of companion diagnostics based on existing economic evaluations of biomarker-guided therapies in cancer. METHODS: A literature search was performed using Medline, Embase, EconLit, Cochrane library for economic evaluations of biomarker-guided therapies with companion diagnostics in cancer. Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies were selected using pre-specified eligibility criteria based on the PICO framework. To make the included studies more comparable, we qualitatively synthesized the data under nine domains of methods where consensus was deemed lacking. RESULTS: Only four of the twenty-two studies included in this review were found to be of good quality with respect to incorporating the characteristics of companion biomarkers in economic evaluations. However, many evaluations focused on a pre-selected patient group rather than including all patients regardless of their biomarker status. Companion biomarker characteristics captured in evaluations were often limited to the cost or the accuracy of the test. Often, only the costs of biomarker testing were modelled. Clinical outcomes and health state utilities were often not included due to the limited data generated by clinical trials. Methods of economic evaluation were not applied consistently in assessments of companion cancer biomarkers for targeted therapies. It was also shown that conflicting cost-effectiveness results were likely depending on what comparator arm was chosen and what comparison structure was designed in the model. CONCLUSION: We found no consistent approach applied in assessing the value of companion biomarker tests and including the characteristics of biomarkers in an economic evaluation of targeted oncology therapies. Currently, many economic evaluations fail to capture the full value of companion biomarkers beyond sensitivity/specificity and cost related to biomarker testing.

High-dimensional single-index models with censored responses.

In this article, we study the estimation of high-dimensional single index models when the response variable is censored. We hybrid the estimation methods for high-dimensional single-index models (but without censorship) and univariate nonparametric models with randomly censored responses to estimate the index parameters and the link function and apply the proposed methods to analyze a genomic dataset from a study of diffuse large B-cell lymphoma. We evaluate the finite sample performance of the proposed procedures via simulation studies and establish large sample theories for the proposed estimators of the index parameter and the nonparametric link function under certain regularity conditions.

Signaling pathway profiling by reverse-phase protein array for personalized cancer medicine.

Deregulation of intracellular signaling through accumulation of genetic alterations is a hallmark of cancer. In the past few decades, concerted and systematic efforts have been made to identify key genetic alterations and to develop therapeutic agents targeting active signaling molecules. However, the efficacy of molecular therapeutics often varies among individuals, and precise mapping of active molecules in individual patients is now considered an essential for therapy optimization. Reverse-phase protein array or microarray (RPPA or RPPM) is an emerging antibody-based highly quantitative proteomic technology, especially suitable for profiling of expression and modification of signaling proteins in low abundance. Because the supply of clinical materials is often limited, RPPA technology is highly advantageous for clinical proteomics in view of its high sensitivity as well as accurate quantification. RPPA has now begun to be incorporated into various clinical trials employing molecular-targeted therapeutics. In this article we review and discuss the application of RPPA technology in the fields of basic, preclinical, and clinical research. The RPPA Global Workshop was recently launched to accelerate the exchange of rapidly expanding knowledge of this fascinating technology among academic laboratories and industries worldwide. This article is part of a Special Issue entitled: Medical Proteomics.

Antiadenovirus Antibodies Predict Response Durability to Nadofaragene Firadenovec Therapy in BCG-unresponsive Non-muscle-invasive Bladder Cancer: Secondary Analysis of a Phase 3 Clinical Trial.

A recent phase 3 trial of intravesical nadofaragene firadenovec reported a promising complete response rate for patients with bacillus Calmette-Guérin-unresponsive non-muscle-invasive bladder cancer. This study examined the ability of antiadenovirus antibody levels to predict the durability of therapeutic response to nadofaragene firadenovec. A standardized and validated quantitative assay was used to prospectively assess baseline and post-treatment serum antibody levels among 91 patients from the phase 3 trial, of whom 47 (52%) were high-grade recurrence free at 12 mo (responders). While baseline titers did not predict treatment response, 3-mo titer >800 was associated with a higher likelihood of durable response (p = 0.026). Peak post-treatment titers >800 were noted in 42 (89%) responders versus 26 (59%) nonresponders (p = 0.001; assay sensitivity, 89%; negative predictive value, 78%). Moreover, 22 (47%) responders compared with eight (18%) nonresponders had a combination of peak post-treatment titers >800 and peak antibody fold change >8 (p = 0.004; assay specificity, 82%; positive predictive value, 73%). A majority of responders continued to have post-treatment antibody titers >800 after the first 6 mo of therapy. In conclusion, serum antiadenovirus antibody quantification may serve as a novel predictive marker for nadofaragene firadenovec response durability. Future studies will focus on large-scale validation and clinical utility of the assay. PATIENT SUMMARY: This study reports on a planned secondary analysis of a phase 3 multicenter clinical trial that established the benefit of nadofaragene firadenovec, a novel intravesical gene therapeutic, for the treatment of patients with bacillus Calmette-Guérin (BCG)-unresponsive high-risk non-muscle-invasive bladder cancer. Prospective assessment of serum anti-human adenovirus type-5 antibody levels of patients in this trial indicated that a combination of post-treatment titers and fold change from baseline can predict treatment efficacy. While this merits additional validation, our findings suggest that serum antiadenovirus antibody levels can serve as an important predictive marker for the durability of therapeutic response to nadofaragene firadenovec.

Consensus Molecular Subtypes Efficiently Classify Gastric Adenocarcinomas and Predict the Response to Anti-PD-1 Immunotherapy.

Background: Gastric adenocarcinoma (GAC) is highly heterogeneous and closely related to colorectal cancer (CRC) both molecularly and functionally. GAC is currently subtyped using a system developed by TCGA. However, with the emergence of immunotherapies, this system has failed to identify suitable treatment candidates. Methods: Consensus molecular subtypes (CMSs) developed for CRC were used for molecular subtyping in GAC based on public expression cohorts, including TCGA, ACRG, and a cohort of GAC patients treated with the programmed cell death 1 (PD-1) inhibitor pembrolizumab. All aspects of each subtype, including clinical outcome, molecular characteristics, oncogenic pathway activity, and the response to immunotherapy, were fully explored. Results: CMS classification was efficiently applied to GAC. CMS4, characterized by EMT activation, stromal invasion, angiogenesis, and the worst clinical outcomes (median OS 24.2 months), was the predominant subtype (38.8%~44.3%) and an independent prognostic indicator that outperformed classical TCGA subtyping. CMS1 (20.9%~21.5%) displayed hypermutation, low SCNV, immune activation, and best clinical outcomes (median OS > 120 months). CMS3 (17.95%~25.7%) was characterized by overactive metabolism, KRAS mutation, and intermediate outcomes (median OS 85.6 months). CMS2 (14.6%~16.3%) was enriched for WNT and MYC activation, differentiated epithelial characteristics, APC mutation, lack of ARID1A, and intermediate outcomes (median OS 48.7 months). Notably, CMS1 was strongly correlated with immunotherapy biomarkers and favorable for the anti-PD-1 drug pembrolizumab, whereas CMS4 was poorly responsive but became more sensitive after EMT-based stratification. Conclusions: Our study reveals the practical utility of CMS classification for GAC to improve clinical outcomes and identify candidates who will respond to immunotherapy.

Differential Dependency of Human Pancreatic Cancer Cells on Targeting PTEN via PLK 1 Expression.

Even though the tumour suppressive role of PTEN is well-known, its prognostic implications are ambiguous. The objective of this study was to further explore the function of PTEN expression in human pancreatic cancer. The expression of PTEN has been dominant in various human cancers including pancreatic cancer when compared with their matched normal tissues. The pancreatic cancer cells have been divided into PTEN blockade-susceptible and PTEN blockade-impassible groups dependent on targeting PTEN by altering intracellular signaling. The expression of PTEN has led to varying clinical outcomes of pancreatic cancer based on GEO Series (GSE) data analysis and Liptak's z analysis. Differential dependency to PTEN blockade has been ascertained based on the expression of polo-like kinase1 PLK1 in pancreatic cancer cells. The prognostic value of PTEN also depends on PLK1 expression in pancreatic cancer. Collectively, the present study provides a rationale for targeting PTEN as a promising therapeutic strategy dependent on PLK1 expressions using a companion biomarker discovery platform.

Positive correlation of expression of L-type amino-acid transporter 1 with colorectal tumor progression and prognosis: Higher expression in sporadic colorectal tumors compared with ulcerative colitis-associated neoplasia.

The role of L-type amino-acid transporter 1 (LAT1), an oncofetal protein, in tumor progression is not well known, although it is important for the survival and proliferation of cancer cells. LAT1 expression was immunohistochemically analyzed and compared in sporadic (conventional) colorectal tumors and ulcerative colitis (UC)-associated neoplasia development and progression. LAT1 expression showed a significant stepwise increase in the order: conventional low-grade tubular adenoma, high-grade tubular adenoma, and invasive adenocarcinoma. Similarly, the same increasing trend in LAT1 expression was found in UC-associated low-grade dysplasia, high-grade dysplasia, and adenocarcinoma, whereas expression was significantly lower compared with that in an adenoma-adenocarcinoma series. LAT1 expression was predominant in the upper half of mucosal lesions in low-grade adenoma. This localized difference in LAT1 expression between the upper and lower halves of mucosal lesions disappeared in conventional high-grade adenoma and adenocarcinoma. LAT1 expression in the colorectal mucosa was significantly increased in the order: nontumor mucosa, quiescent phase of UC, and active phase of UC. Considering the histological pattern of Ki-67 labeling, LAT1 expression appeared partly related to cell proliferation, but this was not significant. In relation to the prognosis of patients with sporadic phase IV colorectal adenocarcinoma, this was significantly poorer in the group with high LAT1 expression compared with that with low LAT1 expression. This suggests LAT1 expression may be used as a companion biomarker for anti-cancer therapy targeting the LAT1 molecule in colorectal cancers.

Cancer biomarker discovery and validation.

With the emergence of genomic profiling technologies and selective molecular targeted therapies, biomarkers play an increasingly important role in the clinical management of cancer patients. Single gene/protein or multi-gene "signature"-based assays have been introduced to measure specific molecular pathway deregulations that guide therapeutic decision-making as predictive biomarkers. Genome-based prognostic biomarkers are also available for several cancer types for potential incorporation into clinical prognostic staging systems or practice guidelines. However, there is still a large gap between initial biomarker discovery studies and their clinical translation due to the challenges in the process of cancer biomarker development. In this review we summarize the steps of biomarker development, highlight key issues in successful validation and implementation, and overview representative examples in the oncology field. We also discuss regulatory issues and future perspectives in the era of big data analysis and precision medicine.