NK cell-triggered CCL5/IFNγ-CXCL9/10 axis underlies the clinical efficacy of neoadjuvant anti-HER2 antibodies in breast cancer.

BACKGROUND: The variability in responses to neoadjuvant treatment with anti-HER2 antibodies prompts to personalized clinical management and the development of innovative treatment strategies. Tumor-infiltrating Natural Killer (TI-NK) cells can predict the efficacy of HER2-targeted antibodies independently from clinicopathological factors in primary HER2-positive breast cancer patients. Understanding the mechanism/s underlying this association would contribute to optimizing patient stratification and provide the rationale for combinatorial approaches with immunotherapy. METHODS: We sought to uncover processes enriched in NK cell-infiltrated tumors as compared to NK cell-desert tumors by microarray analysis. Findings were validated in clinical trial-derived transcriptomic data. In vitro and in vivo preclinical models were used for mechanistic studies. Findings were analysed in clinical samples (tumor and serum) from breast cancer patients. RESULTS: NK cell-infiltrated tumors were enriched in CCL5/IFNG-CXCL9/10 transcripts. In multivariate logistic regression analysis, IFNG levels underlie the association between TI-NK cells and pathological complete response to neoadjuvant treatment with trastuzumab. Mechanistically, the production of IFN-É£ by CD16+ NK cells triggered the secretion of CXCL9/10 from cancer cells. This effect was associated to tumor growth control and the conversion of CD16 into CD16-CD103+ NK cells in humanized in vivo models. In human breast tumors, the CD16 and CD103 markers identified lineage-related NK cell subpopulations capable of producing CCL5 and IFN-É£, which correlated with tissue-resident CD8+ T cells. Finally, an early increase in serum CCL5/CXCL9 levels identified patients with NK cell-rich tumors showing good responses to anti-HER2 antibody-based neoadjuvant treatment. CONCLUSIONS: This study identifies specialized NK cell subsets as the source of IFN-É£ influencing the clinical efficacy of anti-HER2 antibodies. It also reveals the potential of serum CCL5/CXCL9 as biomarkers for identifying patients with NK cell-rich tumors and favorable responses to anti-HER2 antibody-based neoadjuvant treatment.

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.

An integrated precision medicine approach in major depressive disorder: a study protocol to create a new algorithm for the prediction of treatment response.

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the most common option among the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. Here we describe the methodology of the PROMPT project that aims at the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. To address this, the project will be organized in 2 phases. Phase 1 will involve 300 patients with MDD already recruited, comprising 150 TRD and 150 responders, considered as extremes phenotypes of response. A deep clinical stratification will be performed for all patients; moreover, a genomic, transcriptomic and miRNomic profiling will be conducted. The data generated will be exploited to develop an innovative algorithm integrating clinical, omics and sex-related data, in order to predict treatment response and TRD development. In phase 2, a new naturalistic cohort of 300 MDD patients will be recruited to assess, under real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase we will investigate shared decision making (SDM) in the context of pharmacogenetic testing and evaluate various needs and perspectives of different stakeholders toward the use of predictive tools for MDD treatment to foster active participation and patients' empowerment. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.

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.

Circulating microRNA profiling is altered in the acute respiratory distress syndrome related to SARS-CoV-2 infection.

One of the hallmarks of SARS-CoV-2 infection is an induced immune dysregulation, in some cases resulting in cytokine storm syndrome and acute respiratory distress syndrome (ARDS). Several physiological parameters are altered as a result of infection and cytokine storm. Among them, microRNAs (miRNAs) might reflect this poor condition since they play a significant role in immune cellular performance including inflammatory responses. Circulating miRNAs in patients who underwent ARDS and needed mechanical ventilation (MV+; n = 15) were analyzed by next generation sequencing in comparison with patients who had COVID-19 poor symptoms but without intensive care unit requirement (MV-; n = 13). A comprehensive in silico analysis by integration with public gene expression dataset and pathway enrichment was performed. Whole miRNA sequencing identified 170 differentially expressed miRNAs between patient groups. After the validation step by qPCR in an independent sample set (MV+ = 10 vs. MV- = 10), the miR-369-3p was found significantly decreased in MV+ patients (Fold change - 2.7). After integrating with gene expression results from COVID-19 patients, the most significant GO enriched pathways were acute inflammatory response, regulation of transmembrane receptor protein Ser/Thr, fat cell differentiation, and regulation of biomineralization and ossification. In conclusion, miR-369-3p was altered in patients with mechanical ventilation requirement in comparison with COVID-19 patients without this requirement. This miRNA is involved in inflammatory response which it can be considered as a prognosis factor for ARDS in COVID-19 patients.

Comparison of Different Methods for Defining Hyperprogressive Disease in NSCLC.

INTRODUCTION: Hyperprogressive disease (HPD) as a consequence of immune checkpoint inhibitors in NSCLC has been reported in multiple studies. However, inconsistent results in incidence and survival outcomes within studies, together with different assessment methods, have led to increasing controversy regarding the concept of HPD. METHODS: Consecutive patients treated with nivolumab (N = 42) or docetaxel (N = 37) were evaluated. HPD was quantified by applying three different methods (tumor growth rate [TGR], tumor growth kinetics [TGK], and Response Evaluation Criteria in Solid Tumors version 1.1 [RECIST 1.1]). HPD rates were compared between and within both cohorts using the different methods. RESULTS: Using TGR, TGK, and RECIST 1.1, we identified seven (16.7%), seven (16.7%), and six (14.3%) patients with HPD in the nivolumab cohort and three (8.1%), four (10.8%), and five (13.6%) in the docetaxel cohort, respectively. We observed a higher concordance between TGR and TGK (90.1%) compared with RECIST 1.1 (31.3% and 37.5% with TGR and TGK, respectively). We found no significant differences in the overall survival between patients with progressive disease and HPD in either cohort. CONCLUSIONS: TGR and TGK revealed high concordance rates for identifying patients with HPD in NSCLC. The incidence of HPD was numerically higher in patients treated with immune checkpoint inhibitors. Standardization of methods for measuring HPD and its exploration in larger studies are needed to establish its clinical meaning in NSCLC.

Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer.

BACKGROUND: Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. However, the machine learning community made recent elaborations on interpretability methods explaining data point-specific decisions of deep learning techniques. We believe that such explanations can assist the need in personalized precision medicine decisions via explaining patient-specific predictions. METHODS: Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-Euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g., distant metastasis in cancer, for each individual patient. RESULTS: We extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset and then apply the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. CONCLUSIONS: The developed method could be potentially highly useful on interpreting classification results in the context of different omics data and prior knowledge molecular networks on the individual patient level, as for example in precision medicine approaches or a molecular tumor board.

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.

Clinical application of genomic high-throughput data: Infrastructural, ethical, legal and psychosocial aspects.

Genomic high-throughput technologies (GHTT) such as next-generation sequencing represent a fast and cost-effective tool toward a more comprehensive understanding of the molecular background of complex diseases. However, technological advances contrast with insufficient application in clinical practice. Thus, patients, physicians, and other professionals are faced with tough challenges that forestall the efficient and effective implementation. With the increasing application of genetic testing, it is of paramount importance that physicians and other professionals in healthcare recognize the restrictions and potential of GHTT, in order to understand and interpret the complex data in the context of health and disease. At the same time, the growing volume and complexity of data is forever increasing the need for sustainable infrastructure and state-of-the-art tools for efficient data management, including their analysis and integration. The large pool of sensitive information remains difficult to interpret and fundamental questions spanning from billing to legal, social, and ethical issues have still not been resolved. Here we summarize and discuss these obstacles in an interdisciplinary context and suggest ways to overcome them. Continuous discussion with clinicians, data managers, biostatisticians, systems medicine experts, ethicists, legal scholars, and patients illuminates the strengths, weakness, and current practices in the pipeline from biomaterial to sequencing and data management. This discussion also highlights the new, cross-disciplinary working collaborations to realize the wide-ranging challenges in clinical genomics including the exceptional demands placed on the staff preparing and presenting the data, as well as the question as to how to report the data and results to patients.

PI3K: A master regulator of brain metastasis-promoting macrophages/microglia.

Mutations and activation of the PI3K signaling pathway in breast cancer cells have been linked to brain metastases. However, here we describe that in some breast cancer brain metastases samples the protein expression of PI3K signaling components is restricted to the metastatic microenvironment. In contrast to the therapeutic effects of PI3K inhibition on the breast cancer cells, the reaction of the brain microenvironment is less understood. Therefore we aimed to quantify the PI3K pathway activity in breast cancer brain metastasis and investigate the effects of PI3K inhibition on the central nervous system (CNS) microenvironment. First, to systematically quantify the PI3K pathway activity in breast cancer brain metastases, we performed a prospective biomarker study using a reverse phase protein array (RPPA). The majority, namely 30 out of 48 (62.5%) brain metastatic tissues examined, revealed high PI3K signaling activity that was associated with a median overall survival (OS) of 9.41 months, while that of patients, whose brain metastases showed only moderate or low PI3K activity, amounted to only 1.93 and 6.71 months, respectively. Second, we identified PI3K as a master regulator of metastasis-promoting macrophages/microglia during CNS colonization; and treatment with buparlisib (BKM120), a pan-PI3K Class I inhibitor with a good blood-brain-barrier penetrance, reduced their metastasis-promoting features. In conclusion, PI3K signaling is active in the majority of breast cancer brain metastases. Since PI3K inhibition does not only affect the metastatic cells but also re-educates the metastasis-promoting macrophages/microglia, PI3K inhibition may hold considerable promise in the treatment of brain metastasis and the respective microenvironment.

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.

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 .

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.