Integrated genomic analysis identifies a genetic mutation model predicting response to immune checkpoint inhibitors in melanoma

Identification of immune-related genes and integrated analysis of immune-cell infiltration in melanoma

OBJECTIVE

This study was conducted to screen out immune-related genes in connection with the prognosis of melanoma, construct a prognosis model and explore the relevant mechanisms.

METHODS AND MATERIALS

1973 genes associated with immune system were derived from the Immport database, and RNA-seq data of melanoma and information of patients were searched from the Xena database. Cox univariate analysis, Lasso analysis and Cox multivariate analysis were used to screen out six genes to construct the model. Then the risk scores were estimated for patients based on our constructed prognosis model. Estimate was used to affirm that the model was about immune infiltration, and CIBERSORT was used to screen out immune cells associated with prognosis. TIDE was applied to predict the efficacy of immunotherapy. Finally, GSE65904 and GSE19234 were used to confirm the effectiveness of the model.

RESULTS

ADCYAP1R1, GPI, NTS might cause poor prognosis while IFITM1, KIR2DL4, LIF were more likely conductive to prognosis of melanoma patients and a model of prognosis was constructed on the basis of these six genes. The effectiveness of the model has been proven by the ROC curve, and the miRNAs targeting the screened genes were found out, suggesting that the immune system might impact on the prognosis of melanoma by T cell CD8+, T cell CD4+ memory and NK cells.

CONCLUSIONS

In this study, the screened six genes were associated with the prognosis of melanoma, which was conductive to clinical prognostic prediction and individualized treatment strategy.

Identifying biomarkers associated with immunotherapy response in melanoma by multi-omics analysis

Despite immune checkpoint inhibitors (ICIs) have shown the greatest success in melanoma treatment, only a subset of melanoma patients responds well to ICIs. Thus, identifying predictive biomarkers for immunotherapy response is crucial. In this study, we took complementary advantages of immunotherapy data and The Cancer Genome Atlas (TCGA) multi-omics data to explore the predictive biomarkers for the response to immunotherapy in melanoma. We first predicted responsive and non-responsive melanomas in the TCGA skin cutaneous melanoma (SKCM) cohort based on both somatic mutation and transcriptome datasets which involved immunotherapy data for melanoma. This method identified 170 responsive and 56 non-responsive melanomas in TCGA-SKCM. Based on the TCGA-SKCM data, we performed a comprehensive comparison of multi-omics molecular features between responsive and non-responsive melanomas. We identified the molecular features significantly associated with immunotherapy response in melanoma at the genome, transcriptome, epigenome, and proteome levels, respectively. Our analysis confirmed certain immunotherapy response-associated biomarkers, such as tumor mutation burden (TMB), copy number alteration (CNA), intratumor heterogeneity (ITH), PD-L1 expression, and tumor immunity. Moreover, we identified some novel molecular features associated with immunotherapy response: (1) the activation of mast cells and dendritic cells correlating negatively with immunotherapy response; (2) the enrichment of many oncogenic pathways correlating positively with immunotherapy response, such as JAK-STAT, RAS, MAPK, HIF-1, PI3K-Akt, and VEGF pathways; and (3) a number of microRNAs and proteins whose expression correlates with immunotherapy response. In addition, the mTOR signaling pathway has a negative association with immunotherapy response. The novel biomarkers have potential predictive values in immunotherapy response and warrant further investigation.

Comparison of efficacy and tolerability of adjuvant therapy for resected high-risk stage III-IV cutaneous melanoma: a systemic review and Bayesian network meta-analysis

Background

Although immune checkpoint inhibitors (ICIs) and targeted therapies have been widely used as adjuvant treatment for resected melanoma, the optimal therapy remains controversial. Therefore, we conducted this updated network meta-analysis (NMA) to assess the efficacy and tolerability of adjuvant therapies for cutaneous melanoma.

Methods

PubMed, Embase, Cochrane library, and Web of Science were systematically searched for relevant literatures published in the last 30 years. Disease-free survival (DFS), overall survival (OS), and serious adverse events were considered as the efficacy and tolerability outcomes.

Results

In all, 27 randomized controlled trials (RCTs) including 16,709 stage III-IV melanoma patients were enrolled in this NMA. For BRAF wild-type melanoma, our analysis showed that both nivolumab and pembrolizumab demonstrated significantly better DFS and tolerability than ipilimumab (10 mg/kg). Nivolumab, pembrolizumab, ipilimumab (3 mg/kg), and ipilimumab (10 mg/kg) all appeared to be effective in prolonging OS, but no therapy demonstrated significantly better OS than ipilimumab (10 mg/kg). Nivolumab + ipilimumab showed the best DFS, but did not appear to be effective in improving OS and ranked only seventh in tolerability. Vaccines and granulocyte-macrophage colony-stimulating factor therapies were well tolerated, but all failed to improve the DFS or OS in stage III melanoma patients. In terms of BRAF mutation-positive melanoma, ICIs (nivolumab + ipilimumab, nivolumab, pembrolizumab, ipilimumab; 10 mg/kg) exhibited comparable efficacy to dabrafenib + trametinib, and all these therapies showed significantly better DFS than placebo.

Conclusion

Considering efficacy and tolerability, nivolumab and pembrolizumab seem to be preferable adjuvant therapies for patients with stage III-IV melanoma. For BRAF mutation-positive patients, more RCTs are still required to determine which is better between ICIs and targeted therapy.

An enhanced genetic mutation-based model for predicting the efficacy of immune checkpoint inhibitors in patients with melanoma

Background

Programmed death ligand 1 (PD-L1) and tumor mutation burden (TMB) have been developed as biomarkers for the treatment of immune checkpoint inhibitors (ICIs). However, some patients who are TMB-high or PD-L1-high remained resistant to ICIs therapy. Therefore, a more clinically applicable and effective model for predicting the efficacy of ICIs is urgently needed.

Methods

In this study, genomic data for 466 patients with melanoma treated with ICIs from seven independent cohorts were collected and used as training and validation cohorts (training cohort n = 300, validation cohort1 n = 61, validation cohort2 n = 105). Ten machine learning classifiers, including Random Forest classifier, Stochastic Gradient Descent (SGD) classifier and Linear Support Vector Classifier (SVC), were subsequently evaluated.

Results

The Linear SVC with a 186-gene mutation-based set was screened to construct the durable clinical benefit (DCB) model. Patients predicted to have DCB (pDCB) were associated with a better response to the treatment of ICIs in the validation cohort1 (AUC=0.838) and cohort2 (AUC=0.993). Compared with TMB and other reported genetic mutation-based signatures, the DCB model showed greater predictive power. Furthermore, we explored the genomic features in determining the benefits of ICIs treatment and found that patients with pDCB were associated with higher tumor immunogenicity.

Conclusion

The DCB model constructed in this study can effectively predict the efficacy of ICIs treatment in patients with melanoma, which will be helpful for clinical decision-making.

A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma

Background: Ferroptosis is an iron-dependent cell death mode and closely linked to various cancers, including skin cutaneous melanoma (SKCM). Although attempts have been made to construct ferroptosis-related gene (FRG) signatures for predicting the prognosis of SKCM, the prognostic impact of ferroptosis-related genetic mutations in SKCM remains lacking. This study aims to develop a prediction model to explain the relationship between ferroptosis-related genetic mutations and clinical outcomes of SKCM patients and to explore the potential value of ferroptosis in SKCM treatment. Methods: FRGs which significantly correlated with the prognosis of SKCM were firstly screened based on their single-nucleotide variant (SNV) status by univariate Cox regression analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) and Cox regressions were performed to construct a new ferroptosis-related genetic mutation risk (FerrGR) model for predicting the prognosis of SKCM. We then illustrate the survival and receiver operating characteristic (ROC) curves to evaluate the predictive power of the FerrGR model. Moreover, independent prognostic factors, genomic and clinical characteristics, immunotherapy, immune infiltration, and sensitive drugs were compared between high-and low-FerrGR groups. Results: The FerrGR model was developed with a good performance on survival and ROC analysis. It was a robust independent prognostic indicator and followed a nomogram constructed to predict prognostic outcomes for SKCM patients. Besides, FerrGR combined with tumor mutational burden (TMB) or MSI (microsatellite instability) was considered as a combined biomarker for immunotherapy response. The high FerrGR group patients were associated with an inhibitory immune microenvironment. Furthermore, potential drugs target to high FerrGR samples were predicted. Conclusion: The FerrGR model is valuable to predict prognosis and immunotherapy in SKCM patients. It offers a novel therapeutic option for SKCM.

Objective response to immune checkpoint inhibitor therapy in NRAS-mutant melanoma: A systematic review and meta-analysis

Introduction

NRAS mutations are common in melanoma and confer a worse prognosis. Although most patients with metastatic melanoma receive immune checkpoint inhibitors (ICIs), the impact of NRAS mutational status on their efficacy remains under debate.

Methods

We performed a comprehensive literature search across several large databases. Inclusion criteria were trials, cohorts, and large case series that analyzed the primary outcome of objective response rate by NRAS mutational status in patients with melanoma treated with any line of ICI. At least two reviewers independently screened studies using Covidence software, extracted data, and assessed risk of bias. Standard meta-analysis was performed in R with sensitivity analysis and tests for bias.

Results

Data on 1770 patients from ten articles were pooled for meta-analysis, and the objective response rate to ICIs was calculated to compare NRAS-mutant and NRAS-wildtype melanoma. The objective response rate was 1.28 (95% confidence interval: 1.01-1.64). Sensitivity analysis identified the study by Dupuis et al. with influential impact on the pooled effect size and heterogeneity, favoring NRAS-mutant melanoma.

Discussion

In this meta-analysis evaluating the impact of NRAS mutational status on objective response to ICIs in metastatic melanoma, NRAS-mutant cutaneous melanoma demonstrated an increased likelihood of partial or complete tumor response, relative to NRAS-wildtype cutaneous melanoma. Genomic screening for NRAS mutations in patients with metastatic melanoma may improve predictive ability when initiating ICIs.

Transforming Diagnosis and Therapeutics Using Cancer Genomics

In past quarter of the century, much has been understood about the genetic variation and abnormal genes that activate cancer in humans. All the cancers somehow possess alterations in the DNA sequence of cancer cell's genome. In present, we are heading toward the era where it is possible to obtain complete genome of the cancer cells for their better diagnosis, categorization and to explore treatment options.

A novel pathway mutation perturbation score predicts the clinical outcomes of immunotherapy

The link between tumor genetic variations and immunotherapy benefits has been widely recognized. Recent studies suggested that the key biological pathways activated by accumulated genetic mutations may act as an effective biomarker for predicting the efficacy of immune checkpoint inhibitor (ICI) therapy. Here, we developed a novel individual Pathway Mutation Perturbation (iPMP) method that measures the pathway mutation perturbation level by combining evidence of the cumulative effect of mutated genes with the position of mutated genes in the pathways. In iPMP, somatic mutations on a single sample were first mapped to genes in a single pathway to infer the pathway mutation perturbation score (PMPscore), and then, an integrated PMPscore profile was produced, which can be used in place of the original mutation dataset to identify associations with clinical outcomes. To illustrate the effect of iPMP, we applied it to a melanoma cohort treated with ICIs and identified seven significant perturbation pathways, which jointly constructed a pathway-based signature. With the signature, patients were classified into two subgroups with significant distinctive overall survival and objective response rate to immunotherapy. Moreover, the pathway-based signature was consistently validated in two independent melanoma cohorts. We further applied iPMP to two non-small cell lung cancer cohorts and also obtained good performance. Altogether, the iPMP method could be used to identify the significant mutation perturbation pathways for constructing the pathway-based biomarker to predict the clinical outcomes of immunotherapy. The iPMP method has been implemented as a freely available R-based package (https://CRAN.R-project.org/package=PMAPscore).

Integrative analysis reveals a clinicogenomic landscape associated with liver metastasis and poor prognosis in hepatoid adenocarcinoma of the stomach

Hepatoid adenocarcinoma of the stomach (HAS) is a rare subtype of gastric cancer (GC) that histologically resembles hepatocellular carcinoma (HCC). Despite its low incidence, HAS had a poor 5-year survival rate. Currently, the linkages between clinicopathological and genomic features of HAS and its therapeutic targets remain largely unknown. Herein, we enrolled 90 HAS patients and 270 stage-matched non-HAS patients from our institution for comparing clinicopathological features. We found that HAS had worse overall survival and were more prone to develop liver metastasis than non-HAS in our cohort, which was validated via meta-analysis. By comparing whole-exome sequencing data of HAS (n=30), non-HAS (n=63), and HCC (n=355, The Cancer Genome Atlas), we identified a genomic landscape associated with unfavorable clinical features in HAS, which contained frequent somatic mutations and widespread copy number variations. Notably, signaling pathways regulating pluripotency of stem cells affected by frequent genomic alterations might contribute to liver metastasis and poor prognosis in HAS patients. Furthermore, HAS developed abundant multiclonal architecture associated with liver metastasis. Encouragingly, target analysis suggested that HAS patients might potentially benefit from anti-ERBB2 or anti-PD-1 therapy. Taken together, this study systematically demonstrated a high risk of liver metastasis and poor prognosis in HAS, provided a clinicogenomic landscape underlying these unfavorable clinical features, and identified potential therapeutic targets, laying the foundations for developing precise diagnosis and therapy in this rare but lethal disease.

Exploring immunotherapy in colorectal cancer

Chemotherapy combined with or without targeted therapy is the fundamental treatment for metastatic colorectal cancer (mCRC). Due to the adverse effects of chemotherapeutic drugs and the biological characteristics of the tumor cells, it is difficult to make breakthroughs in traditional strategies. The immune checkpoint blockades (ICB) therapy has made significant progress in the treatment of advanced malignant tumors, and patients who benefit from this therapy may obtain a long-lasting response. Unfortunately, immunotherapy is only effective in a limited number of patients with microsatellite instability-high (MSI-H), and segment initial responders can subsequently develop acquired resistance. From September 4, 2014, the first anti-PD-1/PD-L1 drug Pembrolizumab was approved by the FDA for the second-line treatment of advanced malignant melanoma. Subsequently, it was approved for mCRC second-line treatment in 2017. Immunotherapy has rapidly developed in the past 7 years. The in-depth research of the ICB treatment indicated that the mechanism of colorectal cancer immune-resistance has become gradually clear, and new predictive biomarkers are constantly emerging. Clinical trials examining the effect of immune checkpoints are actively carried out, in order to produce long-lasting effects for mCRC patients. This review summarizes the treatment strategies for mCRC patients, discusses the mechanism and application of ICB in mCRC treatment, outlines the potential markers of the ICB efficacy, lists the key results of the clinical trials, and collects the recent basic research results, in order to provide a theoretical basis and practical direction for immunotherapy strategies.

Adjuvant and Neoadjuvant Therapies in Cutaneous Melanoma

Melanoma is the most common cause of skin cancer-related death in the United States. Cutaneous melanoma is most prevalent in the head and neck. The long-term prognosis has been poor and chemotherapy is not curative. Complete surgical resection with locally advanced disease can be challenging and melanoma is resistant to radiation. Advances made in immunotherapy and genomically targeted therapy have transformed the treatment of metastatic melanoma; as of 2021, the 5-year survival for metastatic melanoma is greater than 50%. Ongoing clinical studies are underway to integrate these life-saving therapies into the presurgical or postsurgical settings. This article reviews that effort.

Tumor immunotherapies by immune checkpoint inhibitors (ICIs); the pros and cons

The main breakthrough in tumor immunotherapy was the discovery of immune checkpoint (IC) proteins, which act as a potent suppressor of the immune system by a myriad of mechanisms. After that, scientists focused on the immune checkpoint molecules mainly. Thereby, much effort was spent to progress novel strategies for suppressing these inhibitory axes, resulting in the evolution of immune checkpoint inhibitors (ICIs). Then, ICIs have become a promising approach and shaped a paradigm shift in tumor immunotherapies. CTLA-4 plays an influential role in attenuation of the induction of naïve and memory T cells by engagement with its responding ligands like B7-1 (CD80) and B7-2 (CD86). Besides, PD-1 is predominantly implicated in adjusting T cell function in peripheral tissues through its interaction with programmed death-ligand 1 (PD-L1) and PD-L2. Given their suppressive effects on anti-tumor immunity, it has firmly been documented that ICIs based therapies can be practical and rational therapeutic approaches to treat cancer patients. Nonetheless, tumor inherent or acquired resistance to ICI and some treatment-related toxicities restrict their application in the clinic. The current review will deliver a comprehensive overview of the ICI application to treat human tumors alone or in combination with other modalities to support more desired outcomes and lower toxicities in cancer patients. Video Abstract.

The Predictive Value of Tumor Mutation Burden on Clinical Efficacy of Immune Checkpoint Inhibitors in Melanoma: A Systematic Review and Meta-Analysis

Background: Tumor mutational burden (TMB) is a genomic biomarker that can predict favorable responses to immune checkpoint inhibitors (ICIs). Although we have better understanding of TMB in cancer immunity and cancer immunotherapy, the relationship between TMB and the clinical efficacy of ICIs remains unknown in the treatment of melanoma patients. Here, we conduct a systematic review and meta-analysis to evaluate the predictive value of TMB on the efficacy of ICIs in patients with melanoma.

Methods: We systematically collected data from PubMed, Embase, Cochrane Library, CNKI, China Biomedical Database (CBM), and Wanfang Database. The end date was set to 26 June 2021. We included retrospective studies or clinical trials of ICIs that reported hazard ratios (HRs) for overall survival and/or progression-free survival according to TMB. Data for 1,493 patients from 15 studies were included. In addition, pooled effect size, heterogeneity analysis, sensitivity analysis, publication bias detection, and subgroup analysis were performed based on the included data.

Results: Patients with high TMB showed significantly improved OS (HR = 0.49, 95% CI: 0.33, 0.73; p = 0.001) and PFS (HR = 0.47, 95% CI: 0.33, 0.68; p < 0.001) compared with patients with low TMB. This association was very good in patients treated with monotherapy, that is, anti-CTLA-4 or anti-PD-(L)-1 inhibitors, but not for the patients treated with a combination of the two drugs. The subgroup analysis results showed that heterogeneity was substantial in the targeted next-generation sequencing (NGS) group. Publication bias was detected, and the results were visualized using the funnel chart. And sensitivity analysis and trim-and-fill method analysis showed that our results were stable and reliable.

Conclusion: High TMB is associated with improved OS and PFS in melanoma patients treated with mono-drug ICIs. TMB determined by NGS should be standardized to eliminate heterogeneity. Therefore, the role of TMB in identifying melanoma patients who may benefit from ICI should be further determined in more randomized controlled trials in the future.

Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations

The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refinement of machine learning- and deep learning algorithms; expansion in the depth and variety of databases, including multiomics; and the decreased cost of massively parallelized computational power. Examples of successful clinical applications of AI can be found throughout the cancer continuum and in multidisciplinary practice, with computer vision-assisted image analysis in particular having several U.S. Food and Drug Administration-approved uses. Techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, natural language processing to infer health trajectories from medical notes, and advanced clinical decision support systems that combine genomics and clinomics. Substantial issues have delayed broad adoption, with data transparency and interpretability suffering from AI's "black box" mechanism, and intrinsic bias against underrepresented persons limiting the reproducibility of AI models and perpetuating health care disparities. Midfuture projections of AI maturation involve increasing a model's complexity by using multimodal data elements to better approximate an organic system. Far-future positing includes living databases that accumulate all aspects of a person's health into discrete data elements; this will fuel highly convoluted modeling that can tailor treatment selection, dose determination, surveillance modality and schedule, and more. The field of AI has had a historical dichotomy between its proponents and detractors. The successful development of recent applications, and continued investment in prospective validation that defines their impact on multilevel outcomes, has established a momentum of accelerated progress.

Genome-wide association study of hospitalized COVID-19 patients in the United Arab Emirates

BACKGROUND

The heterogeneity in symptomatology and phenotypic profile attributable to COVID-19 is widely unknown. The objective of this manuscript is to conduct a trans-ancestry genome wide association study (GWAS) meta-analysis of COVID-19 severity to improve the understanding of potentially causal targets for SARS-CoV-2.

METHODS

This cross-sectional study recruited 646 participants in the UAE that were divided into two phenotypic groups based on the severity of COVID-19 phenotypes, hospitalized (n=482) and non-hospitalized (n=164) participants. Hospitalized participants were COVID-19 patients that developed acute respiratory distress syndrome (ARDS), pneumonia or progression to respiratory failure that required supplemental oxygen therapy or mechanical ventilation support or had severe complications such as septic shock or multi-organ failure. We conducted a trans-ancestry meta-analysis GWAS of European (n=302), American (n=102), South Asian (n=99), and East Asian (n=107) ancestry populations. We also carried out comprehensive post-GWAS analysis, including enrichment of SNP associations in tissues and cell-types, expression quantitative trait loci and differential expression analysis.

FINDINGS

Eight genes demonstrated a strong association signal: VWA8 gene in locus 13p14·11 (SNP rs10507497; p=9·54 x10-7), PDE8B gene in locus 5q13·3 (SNP rs7715119; p=2·19 x10-6), CTSC gene in locus 11q14·2 (rs72953026; p=2·38 x10-6), THSD7B gene in locus 2q22·1 (rs7605851; p=3·07x10-6), STK39 gene in locus 2q24·3 (rs7595310; p=4·55 x10-6), FBXO34 gene in locus 14q22·3 (rs10140801; p=8·26 x10-6), RPL6P27 gene in locus 18p11·31 (rs11659676; p=8·88 x10-6), and METTL21C gene in locus 13q33·1 (rs599976; p=8·95 x10-6). The genes are expressed in the lung, associated to tumour progression, emphysema, airway obstruction, and surface tension within the lung, as well as an association to T-cell-mediated inflammation and the production of inflammatory cytokines.

INTERPRETATION

We have discovered eight highly plausible genetic association with hospitalized cases in COVID-19. Further studies must be conducted on worldwide population genetics to facilitate the development of population specific therapeutics to mitigate this worldwide challenge.

FUNDING

This review was commissioned as part of a project to study the host cell receptors of coronaviruses funded by Khalifa University's CPRA grant (Reference number 2020-004).

Pan-cancer analyses reveal that increased Hedgehog activity correlates with tumor immunosuppression and resistance to immune checkpoint inhibitors

Background

Immune checkpoint inhibitors (ICIs) have shown numerous clinical benefits in multiple cancer types, but good predictive biomarkers are severely lacking. Although increasing evidence has linked Hedgehog (Hh) signaling pathway with tumor development, a systematic investigation for its potential as a biomarker remains elusive.

Methods

We collected and analyzed the transcriptional data and clinical outcomes of diverse cancers from the Cancer Genome Atlas and four published ICI datasets. Hh activity was estimated by conducting a single‐sample gene‐set enrichment analysis (ssGSEA) for the Hh‐related genes and calculating the ssGSEA score in each tumor sample.

Results

Our findings suggest that tumors with high Hh activity displayed multiple immunosuppressive characteristics, including lack of anti‐tumor response pathways, downregulation of immune effectors, enrichment of immunosuppressive cells and chemokines, and activation of immunosuppressive signaling. Notably, patients in the non‐response group had enriched Hh activity and showed worse overall survival (OS; pooled HR = 1.50, 95% CI = 1.02–2.21, p = 0.039). In the subgroup of high programmed cell death ligand 1 (PD‐L1) expression, patients who harbored high Hh activity displayed a dramatically lower response rate to ICIs and a strikingly worse OS (pooled HR = 2.89, 95% CI = 1.53–5.49, p < 0.001).

Conclusion

Increased Hh activity correlates with tumor immunosuppression across diverse cancers. Hh activity is not only a predictive biomarker for resistance to ICIs but can also better predict clinical outcomes in combination with PD‐L1 expression.

SerpinB10, a Serine Protease Inhibitor, Is Implicated in UV-Induced Cellular Response

UV-induced DNA damage response and repair are extensively studied processes, as any malfunction in these pathways contributes to the activation of tumorigenesis. Although several proteins involved in these cellular mechanisms have been described, the entire repair cascade has remained unexplored. To identify new players in UV-induced repair, we performed a microarray screen, in which we found SerpinB10 (SPB10, Bomapin) as one of the most dramatically upregulated genes following UV irradiation. Here, we demonstrated that an increased mRNA level of SPB10 is a general cellular response following UV irradiation regardless of the cell type. We showed that although SPB10 is implicated in the UV-induced cellular response, it has no indispensable function in cell survival upon UV irradiation. Nonetheless, we revealed that SPB10 might be involved in delaying the duration of DNA repair in interphase and also in S-phase cells. Additionally, we also highlighted the interaction between SPB10 and H3. Based on our results, it seems that SPB10 protein is implicated in UV-induced stress as a “quality control protein”, presumably by slowing down the repair process.

A genomic mutation signature predicts the clinical outcomes of immunotherapy and characterizes immunophenotypes in gastrointestinal cancer

The association between genetic variations and immunotherapy benefit has been widely recognized, while such evidence in gastrointestinal cancer remains limited. We analyzed the genomic profile of 227 immunotherapeutic gastrointestinal cancer patients treated with immunotherapy, from the Memorial Sloan Kettering (MSK) Cancer Center cohort. A gastrointestinal immune prognostic signature (GIPS) was constructed using LASSO Cox regression. Based on this signature, patients were classified into two subgroups with distinctive prognoses (p < 0.001). The prognostic value of the GIPS was consistently validated in the Janjigian and Pender cohort (N = 54) and Peking University Cancer Hospital cohort (N = 92). Multivariate analysis revealed that the GIPS was an independent prognostic biomarker. Notably, the GIPS-high tumor was indicative of a T-cell-inflamed phenotype and immune activation. The findings demonstrated that GIPS was a powerful predictor of immunotherapeutic survival in gastrointestinal cancer and may serve as a potential biomarker guiding immunotherapy treatment decisions.

Beyond Tumor Mutation Burden: Tumor Neoantigen Burden as a Biomarker for Immunotherapy and Other Types of Therapy

Immunotherapy has significantly improved the clinical outcome of patients with cancer. However, the immune response rate varies greatly, possibly due to lack of effective biomarkers that can be used to distinguish responders from non-responders. Recently, clinical studies have associated high tumor neoantigen burden (TNB) with improved outcomes in patients treated with immunotherapy. Therefore, TNB has emerged as a biomarker for immunotherapy and other types of therapy. In the present review, the potential application of TNB as a biomarker was evaluated. The methods of neoantigen prediction were summarized and the mechanisms involved in TNB were investigated. The impact of high TNB and increased number of infiltrating immune cells on the efficacy of immunotherapy was also addressed. Finally, the future challenges of TNB were discussed.

Precision Medicine and Melanoma: Multi-Omics Approaches to Monitoring the Immunotherapy Response

The treatment and management of patients with metastatic melanoma have evolved considerably in the "era" of personalized medicine. Melanoma was one of the first solid tumors to benefit from immunotherapy; life expectancy for patients in advanced stage of disease has improved. However, many progresses have yet to be made considering the (still) high number of patients who do not respond to therapies or who suffer adverse events. In this scenario, precision medicine appears fundamental to direct the most appropriate treatment to the single patient and to guide towards treatment decisions. The recent multi-omics analyses (genomics, transcriptomics, proteomics, metabolomics, radiomics, etc.) and the technological evolution of data interpretation have allowed to identify and understand several processes underlying the biology of cancer; therefore, improving the tumor clinical management. Specifically, these approaches have identified new pharmacological targets and potential biomarkers used to predict the response or adverse events to treatments. In this review, we will analyze and describe the most important omics approaches, by evaluating the methodological aspects and progress in melanoma precision medicine.

Integrated genomic analysis identifies a genetic mutation model predicting response to immune checkpoint inhibitors in melanoma

Several biomarkers such as tumor mutation burden (TMB), neoantigen load (NAL), programmed cell‐death receptor 1 ligand (PD‐L1) expression, and lactate dehydrogenase (LDH) have been developed for predicting response to immune checkpoint inhibitors (ICIs) in melanoma. However, some limitations including the undefined cut‐off value, poor uniformity of test platform, and weak reliability of prediction have restricted the broad application in clinical practice. In order to identify a clinically actionable biomarker and explore an effective strategy for prediction, we developed a genetic mutation model named as immunotherapy score (ITS) for predicting response to ICIs therapy in melanoma, based on whole‐exome sequencing data from previous studies. We observed that patients with high ITS had better durable clinical benefit and survival outcomes than patients with low ITS in three independent cohorts, as well as in the meta‐cohort. Notably, the prediction capability of ITS was more robust than that of TMB. Remarkably, ITS was not only an independent predictor of ICIs therapy, but also combined with TMB or LDH to better predict response to ICIs than any single biomarker. Moreover, patients with high ITS harbored the immunotherapy‐sensitive characteristics including high TMB and NAL, ultraviolet light damage, impaired DNA damage repair pathway, arrested cell cycle signaling, and frequent mutations in NF1 and SERPINB3/4. Overall, these findings deserve prospective investigation in the future and may help guide clinical decisions on ICIs therapy for patients with melanoma.