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Background Recent evidence suggests that immunotherapy efficacy in melanoma is modulated by gut microbiota. Few studies have examined this phenomenon in humans, and none have incorporated metatranscriptomics, important for determining expression of metagenomic functions in the microbial community. Methods In melanoma patients undergoing immunotherapy, gut microbiome was characterized in pre-treatment stool using 16S rRNA gene and shotgun metagenome sequencing ( n  = 27). Transcriptional expression of metagenomic pathways was confirmed with metatranscriptome sequencing in a subset of 17. We examined associations of taxa and metagenomic pathways with progression-free survival (PFS) using 500 × 10-fold cross-validated elastic-net penalized Cox regression. Results Higher microbial community richness was associated with longer PFS in 16S and shotgun data ( p  < 0.05). Clustering based on overall microbiome composition divided patients into three groups with differing PFS; the low-risk group had 99% lower risk of progression than the high-risk group at any time during follow-up ( p  = 0.002). Among the species selected in regression, abundance of Bacteroides ovatus , Bacteroides dorei , Bacteroides massiliensis , Ruminococcus gnavus , and Blautia producta were related to shorter PFS, and Faecalibacterium prausnitzii , Coprococcus eutactus , Prevotella stercorea , Streptococcus sanguinis , Streptococcus anginosus , and Lachnospiraceae bacterium 3 1 46FAA to longer PFS. Metagenomic functions related to PFS that had correlated metatranscriptomic expression included risk-associated pathways of l -rhamnose degradation, guanosine nucleotide biosynthesis, and B vitamin biosynthesis. Conclusions This work adds to the growing evidence that gut microbiota are related to immunotherapy outcomes, and identifies, for the first time, transcriptionally expressed metagenomic pathways related to PFS. Further research is warranted on microbial therapeutic targets to improve immunotherapy outcomes.

Metastatic cancers produce exosomes that condition pre-metastatic niches in remote microenvironments to favor metastasis. In contrast, here we show that exosomes from poorly metastatic melanoma cells can potently inhibit metastasis to the lung. These “non-metastatic” exosomes stimulate an innate immune response through the expansion of Ly6C low patrolling monocytes (PMo) in the bone marrow, which then cause cancer cell clearance at the pre-metastatic niche, via the recruitment of NK cells and TRAIL-dependent killing of melanoma cells by macrophages. These events require the induction of the Nr4a1 transcription factor and are dependent on pigment epithelium-derived factor (PEDF) on the outer surface of exosomes. Importantly, exosomes isolated from patients with non-metastatic primary melanomas have a similar ability to suppress lung metastasis. This study thus demonstrates that pre-metastatic tumors produce exosomes, which elicit a broad range of PMo-reliant innate immune responses via trigger(s) of immune surveillance, causing cancer cell clearance at the pre-metastatic niche. Exosomes are extracellular vesicles that can favor tumor development and metastasis. Here, the authors show that cancer exosomes may also exert a suppressive function; in fact, exosomes from non-metastatic melanoma cells can lead to the recruitment of patrolling monocytes, which clear cancer cells at the pre-metastatic niche.

Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been considered for inclusion in the American Joint Committee on Cancer (AJCC) staging criteria. However, interobserver discordance continues to prevent the adoption of TIL into clinical practice. Computational image analysis offers a solution to this obstacle, representing a methodological approach for reproducibly counting TIL. We sought to evaluate the ability of a TIL-quantifying machine learning algorithm to predict survival in primary melanoma. Digitized hematoxylin and eosin (H&E) slides from prospectively enrolled patients in the NYU melanoma database were scored for % TIL using machine learning and manually graded by pathologists using Clark's model. We evaluated the association of % TIL with recurrence-free survival (RFS) and overall survival (OS) using Cox proportional hazards modeling and concordance indices. Discordance between algorithmic and manual TIL quantification was assessed with McNemar's test and visually by an attending dermatopathologist. In total, 453 primary melanoma patients were scored using machine learning. Automated % TIL scoring significantly differentiated survival using an estimated cutoff of 16.6% TIL (log-rank P < 0.001 for RFS; P = 0.002 for OS). % TIL was associated with significantly longer RFS (adjusted HR = 0.92 [0.84–1.00] per 10% increase in % TIL) and OS (adjusted HR = 0.90 [0.83–0.99] per 10% increase in % TIL). In comparison, a subset of the cohort (n = 240) was graded for TIL by melanoma pathologists. However, TIL did not associate with RFS between groups (P > 0.05) when categorized as brisk, nonbrisk, or absent. A standardized and automated % TIL scoring algorithm can improve the prognostic impact of TIL. Incorporation of quantitative TIL scoring into the AJCC staging criteria should be considered.

While the association between cross-sectional measures of social isolation and adverse health outcomes is well established, less is known about the association between changes in social isolation and health outcomes. To assess changes of social isolation and mortality, physical function, cognitive function, cardiovascular disease (CVD), and stroke. In a cohort design, social isolation changes in 4 years and subsequent risk of mortality and other outcomes were assessed using the 13 649 eligible Health and Retirement Study (HRS) respondents from the 2006 to 2020 waves. Data were analyzed from October 11, 2023, to April 26, 2024. The main exposure was the change in social isolation measured by the Steptoe 5-item Social Isolation Index from the initial assessment to a second assessment conducted 4 years later. Participants were classified into decreased isolation, stable, or increased isolation groups, stratified by their baseline isolation status. The primary outcomes were mortality, self-reported dependencies in activities of daily living, Alzheimer disease and Alzheimer disease-related dementia, CVD, and stroke. Dementia, CVD, and stroke were assessed using HRS-linked Medicare records. Incidence rates (IRs) of each group were estimated and a Cox proportional hazards regression model was used, with inverse-probability treatment weighting to adjust for confounders. Among 13 649 participants (mean [SD] age at baseline, 65.3 [9.5] years; 8011 [58.7%] women) isolated at baseline, those with increased isolation had higher mortality (n = 693; IR = 68.19; 95% CI, 60.89-76.36 per 1000 person-years) than those who were stable (n = 1796; IR = 44.02; 95% CI, 40.47-47.88 person-years) or had decreased isolation (n = 2067; IR = 37.77; 95% CI, 34.73-41.09 person-years) isolation. Increased isolation was associated with higher risks of mortality (adjusted hazard ratio [AHR], 1.29; 95% CI, 1.09-1.51), disability (AHR, 1.35; 95% CI, 1.09-1.67), and dementia (AHR, 1.40; 95% CI, 1.02-1.93) compared with stable isolation. Similar findings were observed among socially nonisolated participants at baseline. In this cohort study, increased isolation was associated with elevated risks of mortality, disability, and dementia, irrespective of baseline isolation status. These results underscore the importance of interventions targeting the prevention of increased isolation among older adults to mitigate its adverse effects on mortality, as well as physical and cognitive function decline.

Trever Bivona and colleagues show that the Hippo pathway effector YAP promotes resistance to RAF and MEK inhibitor therapy in multiple types of BRAF -mutant tumors. The findings suggest that combined suppression of YAP and RAF-MEK signaling might enhance treatment response and prevent drug resistance. Resistance to RAF- and MEK-targeted therapy is a major clinical challenge 1 , 2 , 3 , 4 . RAF and MEK inhibitors are initially but only transiently effective in some but not all patients with BRAF gene mutation and are largely ineffective in those with RAS gene mutation because of resistance 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 . Through a genetic screen in BRAF-mutant tumor cells, we show that the Hippo pathway effector YAP (encoded by YAP1 ) acts as a parallel survival input to promote resistance to RAF and MEK inhibitor therapy. Combined YAP and RAF or MEK inhibition was synthetically lethal not only in several BRAF-mutant tumor types but also in RAS-mutant tumors. Increased YAP in tumors harboring BRAF V600E was a biomarker of worse initial response to RAF and MEK inhibition in patients, establishing the clinical relevance of our findings. Our data identify YAP as a new mechanism of resistance to RAF- and MEK-targeted therapy. The findings unveil the synthetic lethality of combined suppression of YAP and RAF or MEK as a promising strategy to enhance treatment response and patient survival.

The rationale for using small molecule inhibitors of oncogenic proteins as cancer therapies depends, at least in part, on the assumption that metastatic tumors are primarily clonal with respect to mutant oncogene. With the emergence of BRAF(V600E) as a therapeutic target, we investigated intra- and inter-tumor heterogeneity in melanoma using detection of the BRAF(V600E) mutation as a marker of clonality. BRAF mutant-specific PCR (MS-PCR) and conventional sequencing were performed on 112 tumors from 73 patients, including patients with matched primary and metastatic specimens (n = 18). Nineteen patients had tissues available from multiple metastatic sites. Mutations were detected in 36/112 (32%) melanomas using conventional sequencing, and 85/112 (76%) using MS-PCR. The better sensitivity of the MS-PCR to detect the mutant BRAF(V600E) allele was not due to the presence of contaminating normal tissue, suggesting that the tumor was comprised of subclones of differing BRAF genotypes. To determine if tumor subclones were present in individual primary melanomas, we performed laser microdissection and mutation detection via sequencing and BRAF(V600E)-specific SNaPshot analysis in 9 cases. Six of these cases demonstrated differing proportions of BRAF(V600E)and BRAF(wild-type) cells in distinct microdissected regions within individual tumors. Additional analyses of multiple metastatic samples from individual patients using the highly sensitive MS-PCR without microdissection revealed that 5/19 (26%) patients had metastases that were discordant for the BRAF(V600E) mutation. In conclusion, we used highly sensitive BRAF mutation detection methods and observed substantial evidence for heterogeneity of the BRAF(V600E) mutation within individual melanoma tumor specimens, and among multiple specimens from individual patients. Given the varied clinical responses of patients to BRAF inhibitor therapy, these data suggest that additional studies to determine possible associations between clinical outcomes and intra- and inter-tumor heterogeneity could prove fruitful.

Background Immune checkpoint blockade (ICB) shows lasting benefits in advanced melanoma; however, not all patients respond to this treatment and many develop potentially life-threatening immune-related adverse events (irAEs). Identifying individuals who will develop irAEs is critical in order to improve the quality of care. Here, we prospectively demonstrate that the gut microbiome predicts irAEs in melanoma patients undergoing ICB. Methods Pre-, during, and post-treatment stool samples were collected from 27 patients with advanced stage melanoma treated with IPI (anti-CTLA-4) and NIVO (anti-PD1) ICB inhibitors at NYU Langone Health. We completed 16S rRNA gene amplicon sequencing, DNA deep shotgun metagenomic, and RNA-seq metatranscriptomic sequencing. The divisive amplicon denoising algorithm (DADA2) was used to process 16S data. Taxonomy for shotgun sequencing data was assigned using MetaPhlAn2, and gene pathways were assigned using HUMAnN 2.0. Compositionally aware differential expression analysis was performed using ANCOM. The Cox-proportional hazard model was used to assess the prospective role of the gut microbiome (GMB) in irAES, with adjustment for age, sex, BMI, immune ICB treatment type, and sequencing batch. Results Two natural GMB clusters with distinct community compositions were identified from the analysis of 16S rRNA data ( R 2 = 0.16, p < 0.001). In Cox-proportional hazard modeling, these two clusters showed a near 7-fold differential risk for developing irAEs within 1 year of initiating treatment (HR = 6.89 [95% CI: 1.33–35.58]). Using shotgun metagenomics, we further identified 37 bacterial strains differentially expressed between the risk groups, with specific dominance of Bacteroides dorei within the high-risk GMB cluster and Bacteroides vulgatus in the low-risk cluster. The high-risk cluster also appeared to have elevated expression of several functional pathways, including those associated with adenosine metabolism (all FDR < 0.05). A sub-analysis of samples ( n = 10 participants) at baseline and 6 and 12 weeks after the start of treatment revealed that the microbiome remained stable over the course of treatment ( R 2 = 0.88, p < 0.001). Conclusions We identified two distinct fecal bacterial community clusters which are associated differentially with irAEs in ICB-treated advanced melanoma patients.

BackgroundRecent research suggests that baseline body mass index (BMI) is associated with response to immunotherapy. In this study, we test the hypothesis that worsening nutritional status prior to the start of immunotherapy, rather than baseline BMI, negatively impacts immunotherapy response.MethodsWe studied 629 patients with advanced cancer who received immune checkpoint blockade at New York University. Patients had melanoma (n=268), lung cancer (n=128) or other primary malignancies (n=233). We tested the association between BMI changes prior to the start of treatment, baseline prognostic nutritional index (PNI), baseline BMI category and multiple clinical end points including best overall response (BOR), objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS) and overall survival (OS).ResultsDecreasing pretreatment BMI and low PNI were associated with worse BOR (p=0.04 and p=0.0004), ORR (p=0.01 and p=0.0005), DCR (p=0.01 and p<0.0001), PFS (p=0.02 and p=0.01) and OS (p<0.001 and p<0.001). Baseline BMI category was not significantly associated with any treatment outcomes.ConclusionStandard of care measures of worsening nutritional status more accurately associate with immunotherapy outcomes than static measurements of BMI. Future studies should focus on determining whether optimizing pretreatment nutritional status, a modifiable variable, leads to improvement in immunotherapy response.

Background Immune checkpoint inhibition (ICI) improves survival outcomes for patients with several types of cancer including metastatic melanoma (MM), but serious immune-related adverse events requiring intervention with immunosuppressive medications occur in a subset of patients. Skin toxicity (ST) has been reported to be associated with better response to ICI. However, understudied factors, such as ST severity and potential survivor bias, may influence the strength of these observed associations. Methods To examine the potential confounding impact of such variables, we analyzed advanced cancer patients enrolled prospectively in a clinicopathological database with protocol-driven follow up and treated with ICI. We tested the associations between developing ST, stratified as no (n = 617), mild (n = 191), and severe (n = 63), and progression-free survival (PFS) and overall survival (OS) in univariable and multivariable analyses. We defined severe ST as a skin event that required treatment with systemic corticosteroids. To account for the possibility of longer survival associating with adverse events instead of the reverse, we treated ST as a time-dependent covariate in an adjusted model. Results Both mild and severe ST were significantly associated with improved PFS and OS (all P < 0.001). However, when adjusting for the time from treatment initiation to time of skin event, severe ST was not associated with PFS benefit both in univariable and multivariable analyses (P = 0.729 and P = 0.711, respectively). Receiving systemic steroids for ST did not lead to significant differences in PFS or OS compared to patients who did not receive systemic steroids. Conclusions Our data reveal the influence of time to event and its severity as covariates in analyzing the relationship between ST and ICI outcomes. These differences in outcomes cannot be solely explained by the use of immunosuppressive medications, and thus highlight the importance of host- and disease-intrinsic factors in determining ICI response and toxicity. Trial registration: The patient data used in this manuscript come from patients who were prospectively enrolled in two institutional review board-approved databases at NYU Langone Health (institutional review board #10362 and #S16-00122).

Background Immune checkpoint inhibitors (anti-CTLA-4, anti-PD-1, or the combination) enhance anti-tumor immune responses, yielding durable clinical benefit in several cancer types, including melanoma. However, a subset of patients experience immune-related adverse events (irAEs), which can be severe and result in treatment termination. To date, no biomarker exists that can predict development of irAEs. Methods We hypothesized that pre-treatment antibody profiles identify a subset of patients who possess a sub-clinical autoimmune phenotype that predisposes them to develop severe irAEs following immune system disinhibition. Using a HuProt human proteome array, we profiled baseline antibody levels in sera from melanoma patients treated with anti-CTLA-4, anti-PD-1, or the combination, and used support vector machine models to identify pre-treatment antibody signatures that predict irAE development. Results We identified distinct pre-treatment serum antibody profiles associated with severe irAEs for each therapy group. Support vector machine classifier models identified antibody signatures that could effectively discriminate between toxicity groups with > 90% accuracy, sensitivity, and specificity. Pathway analyses revealed significant enrichment of antibody targets associated with immunity/autoimmunity, including TNFα signaling, toll-like receptor signaling and microRNA biogenesis. Conclusions Our results provide the first evidence supporting a predisposition to develop severe irAEs upon immune system disinhibition, which requires further independent validation in a clinical trial setting.