The Tumor Microbiome as a Predictor of Outcomes in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibitors.

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and its potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICI). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA sequencing (RNA-seq) was conducted on the formalin-fixed, paraffin-embedded and fresh frozen tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival >24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The age of the 71 patients with metastatic melanoma ranged from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy-responsive versus nonresponsive tumors. Responders showed significant enrichment of bacteriophages in the phylum Uroviricota, and nonresponders showed enrichment of several bacteria, including Campylobacter jejuni. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs. SIGNIFICANCE: We analyzed the tumor microbiome and interactions with genes and pathways in metastatic melanoma treated with immunotherapy and identified several microbes associated with immunotherapy response and immune-related gene expression signatures. Machine learning models that combined microbe abundances and gene expression outperformed models using either dataset alone in predicting immunotherapy responses.

SASH1 S519N Variant Links Skin Hyperpigmentation and Premature Hair Graying to Dysfunction of Melanocyte Lineage.

A better understanding of human melanocyte (MC) and MC stem cell biology is essential for treating MC-related diseases. This study employed an inherited pigmentation disorder carrying the SASH1S519N variant in a Hispanic family to investigate SASH1 function in the MC lineage and the underlying mechanism for this disorder. We used a multidisciplinary approach, including clinical examinations, human cell assays, yeast 2-hybrid screening, and biochemical techniques. Results linked early hair graying to the SASH1S519N variant, a previously unrecognized clinical phenotype in hyperpigmentation disorders. In vitro, we identified SASH1 as a regulator in MC stem cell maintenance and discovered that TNKS2 is crucial for SASH1's role. In addition, the S519N variant is located in one of multiple tankyrase-binding motifs and alters the binding kinetics and affinity of the interaction. In summary, this disorder links both gain and loss of pigmentation in the same individual, hinting to accelerated aging in human MC stem cells. The findings offer insights into the roles of SASH1 and TNKS2 in MC stem cell maintenance and the molecular mechanisms of pigmentation disorders. We propose that a comprehensive clinical evaluation of patients with MC-related disorders should include an assessment and history of hair pigmentation loss.

Diverse approaches to predicting drug-induced liver injury using gene-expression profiles.

BACKGROUND: Drug-induced liver injury (DILI) is a serious concern during drug development and the treatment of human disease. The ability to accurately predict DILI risk could yield significant improvements in drug attrition rates during drug development, in drug withdrawal rates, and in treatment outcomes. In this paper, we outline our approach to predicting DILI risk using gene-expression data from Build 02 of the Connectivity Map (CMap) as part of the 2018 Critical Assessment of Massive Data Analysis CMap Drug Safety Challenge. RESULTS: First, we used seven classification algorithms independently to predict DILI based on gene-expression values for two cell lines. Similar to what other challenge participants observed, none of these algorithms predicted liver injury on a consistent basis with high accuracy. In an attempt to improve accuracy, we aggregated predictions for six of the algorithms (excluding one that had performed exceptionally poorly) using a soft-voting method. This approach also failed to generalize well to the test set. We investigated alternative approaches-including a multi-sample normalization method, dimensionality-reduction techniques, a class-weighting scheme, and expanding the number of hyperparameter combinations used as inputs to the soft-voting method. We met limited success with each of these solutions. CONCLUSIONS: We conclude that alternative methods and/or datasets will be necessary to effectively predict DILI in patients based on RNA expression levels in cell lines. REVIEWERS: This article was reviewed by Pawel P Labaj and Aleksandra Gruca (both nominated by David P Kreil).