Immunohistochemical markers as predictors of prognosis in multifocal prostate cancer

The impact of tumor focality on prostate cancer (PCa) prognosis has been addressed in several studies with conflicting results. Tumor foci from multifocal (MF) PCa can show highly heterogeneous molecular features. Our aim was to analyze the protein expression of PTEN, SPOP, SLC45A3, ETV1, ERG and the "triple hit" (ERG overexpression, PTEN plus SLC45A3 loss) in unifocal (UF) and MF PCa, to evaluate their value as prognostic markers according to focality, and the role of tumor heterogeneity in MF disease. PTEN, SPOP, SLC45A3, ETV1 and ERG immunohistochemical expression was evaluated in 185 PCa from 9 TMAs, 51 UF and 134 MF. In a subset of 69 MF cases, the dominant and secondary foci (DF and SF) were compared. Heterogeneity was considered when both tumor foci presented different expression patterns. Relationship with clinicopathological features was also analyzed. MF PCa was diagnosed in significantly younger patients when compared to UF ones (p = 0.007). ETV1 overexpression was associated with UF disease (p = 0.028). A shorter time to PSA recurrence was related to SLC45A3 wt expression in UF PCa (p = 0.052), and to SPOP expression loss (p = 0.043) or "triple hit" phenotype in MF PCa (p = 0.041). In MF cases, PTEN loss, SLC45A3 loss and "triple hit" phenotype were associated with the DF and had significant heterogeneity. In conclusion, our results indicate that UF and MF PCa have relevant and consistent molecular differences. The analysis of an immunohistochemical panel, composed by PTEN, SPOP, SLC45A3, ETV1 and ERG, could be useful to predict outcome in MF cases.

The impact of mutational clonality in predicting the response to immune checkpoint inhibitors in advanced urothelial cancer

Immune checkpoint inhibitors (ICI) have revolutionized cancer treatment and can result in complete remissions even at advanced stages of the disease. However, only a small fraction of patients respond to the treatment. To better understand which factors drive clinical benefit, we have generated whole exome and RNA sequencing data from 27 advanced urothelial carcinoma patients treated with anti-PD-(L)1 monoclonal antibodies. We assessed the influence on the response of non-synonymous mutations (tumor mutational burden or TMB), clonal and subclonal mutations, neoantigen load and various gene expression markers. We found that although TMB is significantly associated with response, this effect can be mostly explained by clonal mutations, present in all cancer cells. This trend was validated in an additional cohort. Additionally, we found that responders with few clonal mutations had abnormally high levels of T and B cell immune markers, suggesting that a high immune cell infiltration signature could be a better predictive biomarker for this subset of patients. Our results support the idea that highly clonal cancers are more likely to respond to ICI and suggest that non-additive effects of different signatures should be considered for predictive models.

Pre-existing tumor host immunity characterization in resected non-small cell lung cancer

INTRODUCTION

Neoadjuvant and adjuvant immune checkpoint blockade (ICB) have recently become standard of care in resectable non-small cell lung cancer (NSCLC). Yet, biomarkers that inform patients who benefit from this approach remain largely unknown. Here, we interrogated the tumor immune microenvironment (TIME) in early-stage NSCLC patients that underwent up-front surgery.

METHODS

A total of 185 treatment-naïve patients with early-stage NSCLC, that underwent up-front surgical treatment between 2006 and 2018 at Hospital del Mar were included. 124 lung adenocarcinomas (LUADs), and 61 squamous cell carcinoma (LUSCs) were included in a tissue microarray. Immunohistochemistry for CD3, CD4, CD8, CD68, CD80, CD103, FOXP3, PD-1, PD-L1, PD-L2 and HLA class II were evaluated by digital image analysis (QuPath software). TIME was categorized into four groups using PD-L1 expression in tumor cells (<1 % or ≥1 %) and tumor resident memory (CD103⁺) immune cells (using the median as cut-off). We explored the association between different TIME dimensions and patient's clinicopathological features and outcomes.

RESULTS

We found increased levels of T cell markers (CD3⁺, CD4⁺, CD8⁺ cells), functional immune markers (FOXP3⁺ cells) as well as, higher HLA-II tumor membrane expression in LUADs compared to LUSCs (p < 0.05 for all). In contrast, LUSCs displayed higher percentage of intratumor macrophages (CD68⁺ cells) as well as, higher PD-L1 and PD-L2 tumor membrane expression (p < 0.05 for all). Unsupervised analysis revealed three different tumor subsets characterized by membrane tumor expression of PD-L1, PD-L2 and HLA-class II. Enrichment of T cells (CD3⁺, CD8⁺ cells), regulatory T cells (FOXP3⁺ cells) and macrophages (CD68⁺ cells) was observed in the CD103⁺/PD-L1⁺ group (p < 0.05 for all). Multivariate analysis showed that infiltration by CD103⁺ immune cells was associated with improved OS (p = 0.009).

CONCLUSIONS

TIME analysis in resected NSCLC highlighted differences by histology, PD-L1 expression and molecular subgroups. Biomarker studies using IHC might aid to individually tailor adjuvant treatment in early-stage NSCLC.

Dynamics of Gene Expression Profiling and Identification of High-Risk Patients for Severe COVID-19

The clinical manifestations of SARS-CoV-2 infection vary widely, from asymptomatic infection to the development of acute respiratory distress syndrome (ARDS) and death. The host response elicited by SARS-CoV-2 plays a key role in determining the clinical outcome. We hypothesized that determining the dynamic whole blood transcriptomic profile of hospitalized adult COVID-19 patients and characterizing the subgroup that develops severe disease and ARDS would broaden our understanding of the heterogeneity in clinical outcomes. We recruited 60 hospitalized patients with RT-PCR-confirmed SARS-CoV-2 infection, among whom 19 developed ARDS. Peripheral blood was collected using PAXGene RNA tubes within 24 h of admission and on day 7. There were 2572 differently expressed genes in patients with ARDS at baseline and 1149 at day 7. We found a dysregulated inflammatory response in COVID-19 ARDS patients, with an increased expression of genes related to pro-inflammatory molecules and neutrophil and macrophage activation at admission, in addition to an immune regulation loss. This led, in turn, to a higher expression of genes related to reactive oxygen species, protein polyubiquitination, and metalloproteinases in the latter stages. Some of the most significant differences in gene expression found between patients with and without ARDS corresponded to long non-coding RNA involved in epigenetic control.

Identifying Actionable Variants in Cancer - The Dual Web and Batch Processing Tool MTB-Report

Next-generation sequencing methods continuously provide clinicians and researchers in precision oncology with growing numbers of genomic variants found in cancer. However, manually interpreting the list of variants to identify reliable targets is an inefficient and cumbersome process that does not scale with the increasing number of cases. Support by computer systems is needed for the analysis of large scale experiments and clinical studies to identify new targets and therapies, and user-friendly applications are needed in molecular tumor boards to support clinicians in their decision-making processes. The MTB-Report tool annotates, filters and sorts genetic variants with information from public databases, providing evidence on actionable variants in both scenarios. A web interface supports medical doctors in the tumor board, and a command line mode allows batch processing of large datasets. The MTB-Report tool is available as an R implementation as well as a Docker image to provide a tool that runs out-of-the-box. Moreover, containerization ensures a stable application that delivers reproducible results over time. A public version of the web interface is available at: http://mtb.bioinf.med.uni-goettingen.de/mtb-report.

Genomic Predictors of Good Outcome, Recurrence, or Progression in High-Grade T1 Non-Muscle-Invasive Bladder Cancer

High-grade T1 (HGT1) bladder cancer is the highest risk subtype of non-muscle-invasive bladder cancer with unpredictable outcome and poorly understood risk factors. Here, we examined the association of somatic mutation profiles with nonrecurrent disease (GO, good outcome), recurrence (R), or progression (PD) in a cohort of HGT1 patients. Exome sequencing was performed on 62 HGT1 and 15 matched normal tissue samples. Both tumor only (TO) and paired analyses were performed, focusing on 95 genes known to be mutated in bladder cancer. Somatic mutations, copy-number alterations, mutation load, and mutation signatures were studied. Thirty-three GO, 10 R, 18 PD, and 1 unknown outcome patients were analyzed. Tumor mutational burden (TMB) was similar to muscle-invasive disease and was highest in GO, intermediate in PD, and lowest in R patients (P = 0.017). DNA damage response gene mutations were associated with higher TMB (P < 0.0001) and GO (P = 0.003). ERCC2 and BRCA2 mutations were associated with GO. TP53, ATM, ARID1A, AHR, and SMARCB1 mutations were more frequent in PD. Focal copy-number gain in CCNE1 and CDKN2A deletion was enriched in PD or R (P = 0.047; P = 0.06). APOBEC (46%) and COSMIC5 (34%) signatures were most frequent. APOBEC-A and ERCC2 mutant tumors (COSMIC5) were associated with GO (P = 0.047; P = 0.0002). pT1b microstaging was associated with a genomic cluster (P = 0.05) with focal amplifications of E2F3/SOX4, PVRL4, CCNE1, and TP53 mutations. Findings were validated using external public datasets. These findings require confirmation but suggest that management of HGT1 bladder cancer may be improved via molecular characterization to predict outcome. SIGNIFICANCE: Detailed genetic analyses of HGT1 bladder tumors identify features that correlate with outcome, e.g., high mutational burden, ERCC2 mutations, and high APOBEC-A/ERCC2 mutation signatures were associated with good outcome.

From somatic variants towards precision oncology: Evidence-driven reporting of treatment options in molecular tumor boards

Background

A comprehensive understanding of cancer has been furthered with technological improvements and decreasing costs of next-generation sequencing (NGS). However, the complexity of interpreting genomic data is hindering the implementation of high-throughput technologies in the clinical context: increasing evidence on gene–drug interactions complicates the task of assigning clinical significance to genomic variants.

Methods

Here we present a method that automatically matches patient-specific genomic alterations to treatment options. The method relies entirely on public knowledge of somatic variants with predictive evidence on drug response. The output report is aimed at supporting clinicians in the task of finding the clinical meaning of genomic variants. We applied the method to 1) The Cancer Genome Atlas (TCGA) and Genomics Evidence Neoplasia Information Exchange (GENIE) cohorts and 2) 11 patients from the NCT MASTER trial whose treatment discussions included information on their genomic profiles.

Results

Our reporting strategy showed a substantial number of patients with actionable variants in the analyses of TCGA and GENIE samples. Notably, it was able to reproduce experts’ treatment suggestions in a retrospective study of 11 patients from the NCT MASTER trial. Our results establish a proof of concept for comprehensive, evidence-based reports as a supporting tool for discussing treatment options in tumor boards.

Conclusions

We believe that a standardized method to report actionable somatic variants will smooth the incorporation of NGS in the clinical context. We anticipate that tools like the one we present here will become essential in summarizing for clinicians the growing evidence in the field of precision medicine. The R code of the presented method is provided in Additional file 6 and available at https://github.com/jperera-bel/MTB-Report.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0529-2) contains supplementary material, which is available to authorized users.

Using RNA-Seq Data for the Detection of a Panel of Clinically Relevant Mutations

Somatic single nucleotide variants (SNVs) are genomic events with increasing implications in cancer treatment. The clinical standard for SNVs detection is whole genome/exome sequencing (WGS/WES) in matched tumor-normal samples. Yet, this is a very costly approach both economically and biologically and very often only tumor samples are sequenced. On the other hand, RNA sequencing (RNA-Seq) is the most popular technology to study gene expression, and has also the potential for a cost-effective identification of SNVs as an alternative to tumor-only WES. Here we present a method for the identification of SNVs in tumor-only RNA-Seq data putting a special focus on a small panel of clinically relevant SNVs. For evaluation purposeswe analyzed matched tumor-normal WEStumor-only RNA-Seq data from 14 cancer patients. We compared SNVs detected in i) RNA-Seq by our method, ii) WES tumor-only by Mutect2 and iii) WES matched tumor-normal by Mutect2. We did a detailed evaluation for a reduced panel of clinically relevant SNVs and reliably identified in RNA-Seq data a subset of mutations for which we had pathological annotation. Hence, RNA-Seq rises as a cost-effective option to detect in parallel gene expression as well as a small panel of clinically relevant SNVs in research.

Large variability of bathypelagic microbial eukaryotic communities across the world's oceans

In this work, we study the diversity of bathypelagic microbial eukaryotes (0.8-20 μm) in the global ocean. Seawater samples from 3000 to 4000 m depth from 27 stations in the Atlantic, Pacific and Indian Oceans were analyzed by pyrosequencing the V4 region of the 18S ribosomal DNA. The relative abundance of the most abundant operational taxonomic units agreed with the results of a parallel metagenomic analysis, suggesting limited PCR biases in the tag approach. Although rarefaction curves for single stations were seldom saturated, the global analysis of all sequences together suggested an adequate recovery of bathypelagic diversity. Community composition presented a large variability among samples, which was poorly explained by linear geographic distance. In fact, the similarity between communities was better explained by water mass composition (26% of the variability) and the ratio in cell abundance between prokaryotes and microbial eukaryotes (21%). Deep diversity appeared dominated by four taxonomic groups (Collodaria, Chrysophytes, Basidiomycota and MALV-II) appearing in different proportions in each sample. Novel diversity amounted to 1% of the pyrotags and was lower than expected. Our study represents an essential step in the investigation of bathypelagic microbial eukaryotes, indicating dominating taxonomic groups and suggesting idiosyncratic assemblages in distinct oceanic regions.