Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors

Predictive role of ctDNA in esophageal squamous cell carcinoma receiving definitive chemoradiotherapy combined with toripalimab

The combination of toripalimab (an anti-PD-1 antibody) with definitive chemoradiotherapy (CRT) demonstrated encouraging efficacy against locally advanced esophageal squamous cell carcinoma (ESCC) in the EC-CRT-001 phase II trial (NCT04005170). The primary endpoint of this trial was the clinical complete response rate (cCR), and the secondary endpoints included overall survival (OS), progression-free survival (PFS), duration of response, and quality of life. The exploratory analyses of EC-CRT-001 include exploring the role of circulating tumor DNA (ctDNA) and blood-based tumor mutational burden (bTMB) in predicting the response and survival. In total, 118 blood and 35 tissue samples from 42 enrolled patients were included in the analyses. We found that ctDNA-negative patients achieved a higher cCR compared to those with detectable ctDNA during CRT (83%, 19/23 vs. 39%, 7/18; p = 0.008) or post-CRT (78%, 21/27 vs. 30%, 3/10; p = 0.017). Patients with detectable ctDNA during CRT had shorter PFS (p = 0.014). Similarly, patients with post-CRT detectable ctDNA had a significantly shorter PFS (p = 0.012) and worse OS (p = 0.004). Moreover, patients with high bTMB levels during CRT had prolonged OS (p = 0.027). In conclusion, ctDNA and bTMB have the potential to predict treatment efficacy and survival in ESCC treated with CRT and immunotherapy.

The Future of Targeted Therapy for Leiomyosarcoma

Leiomyosarcoma (LMS) is an aggressive subtype of soft tissue sarcoma that arises from smooth muscle cells, most commonly in the uterus and retroperitoneum. LMS is a heterogeneous disease with diverse clinical and molecular characteristics that have yet to be fully understood. Molecular profiling has uncovered possible targets amenable to treatment, though this has yet to translate into approved targeted therapies in LMS. This review will explore historic and recent findings from molecular profiling, highlight promising avenues of current investigation, and suggest possible future strategies to move toward the goal of molecularly matched treatment of LMS. We focus on targeting the DNA damage response, the macrophage-rich micro-environment, the PI3K/mTOR pathway, epigenetic regulators, and telomere biology.

CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer

BACKGROUND

Tumor mutational burden (TMB) is one of the most significant predictive biomarkers of immunotherapy efficacy in non-small cell lung cancer (NSCLC). Radiomics allows high-throughput extraction and analysis of advanced and quantitative medical imaging features. This study develops and validates a radiomic model for predicting TMB level and the response to immunotherapy based on CT features in NSCLC.

METHOD

Pre-operative chest CT images of 127 patients with NSCLC were retrospectively studied. The 3D-Slicer software was used to outline the region of interest and extract features from the CT images. Radiomics prediction model was constructed by LASSO and multiple logistic regression in a training dataset. The model was validated by receiver operating characteristic (ROC) curves and calibration curves using external datasets. Decision curve analysis was used to assess the value of the model for clinical application.

RESULTS

A total of 1037 radiomic features were extracted from the CT images of NSCLC patients from TCGA. LASSO regression selected three radiomics features (Flatness, Autocorrelation and Minimum), which were associated with TMB level in NSCLC. A TMB prediction model consisting of 3 radiomic features was constructed by multiple logistic regression. The area under the curve (AUC) value in the TCGA training dataset was 0.816 (95% CI: 0.7109-0.9203) for predicting TMB level in NSCLC. The AUC value in external validation dataset I was 0.775 (95% CI: 0.5528-0.9972) for predicting TMB level in NSCLC, and the AUC value in external validation dataset II was 0.762 (95% CI: 0.5669-0.9569) for predicting the efficacy of immunotherapy in NSCLC.

CONCLUSION

The model based on CT radiomic features helps to achieve cost effective improvement in TMB classification and precise immunotherapy treatment of NSCLC patients.

Case report: Successful treatment of advanced pulmonary sarcomatoid carcinoma with BUBIB-ALK rearrangement and KRAS G12C mutation by sintilimab combined with anlotinib

Pulmonary sarcomatoid carcinoma (PSC) is a rare and aggressive subtype of non-small cell lung cancer (NSCLC) that is characterized by poor differentiation and invasiveness. According to the World Health Organization, PSC exhibits sarcoma or sarcomatoid differentiation and typically presents with an insidious onset, lacking specific symptoms and signs. It is associated with high malignancy, early metastasis, short survival time, and a poor prognosis. Treatment for PSC follows a similar approach to NSCLC; however, it presents significant challenges due to its high resistance to chemotherapy. Previous research has demonstrated the coexistence of two or more target mutations in PSC, and the presence of multiple mutations is correlated with higher mortality rates compared to single mutations. This is supported by our case study of a male patient with advanced BUBIB-ALK rearrangement and KRAS G12C missense mutation. There is currently no standard treatment protocol available for patients with this condition. The patient showed rapid progression after 1 month of alectinib treatment and was intolerant to paclitaxel + cisplatin chemotherapy. Following this, successful disease control was achieved with a combination therapy of sintilimab and anlotinib. The patient achieved a progression-free survival (PFS) of over 20 months, and long-term follow-up is still ongoing for the patient. Based on our clinical experience, the combination of anlotinib and programmed death-1 (PD-1) inhibitors may be a promising strategy for PSC patients, particularly those with multi-target mutations who do not respond to ALK-TKI and are resistant to chemotherapy.

Precision treatment in advanced hepatocellular carcinoma

The past decade has witnessed significant advances in the systemic treatment of advanced hepatocellular carcinoma (HCC). Nevertheless, the newly developed treatment strategies have not achieved universal success and HCC patients frequently exhibit therapeutic resistance to these therapies. Precision treatment represents a paradigm shift in cancer treatment in recent years. This approach utilizes the unique molecular characteristics of individual patient to personalize treatment modalities, aiming to maximize therapeutic efficacy while minimizing side effects. Although precision treatment has shown significant success in multiple cancer types, its application in HCC remains in its infancy. In this review, we discuss key aspects of precision treatment in HCC, including therapeutic biomarkers, molecular classifications, and the heterogeneity of the tumor microenvironment. We also propose future directions, ranging from revolutionizing current treatment methodologies to personalizing therapy through functional assays, which will accelerate the next phase of advancements in this area.

Addition of PD-1/PD-L1 inhibitors to chemotherapy for triple-negative breast cancer: a meta-analysis

Background

In recent years, the addition of immune checkpoint inhibitors (ICI) to chemotherapy (CT) has become a research hotspot in the therapy of metastatic triple-negative breast cancer. Nevertheless, controversial results have been revealed among the published randomized controlled trials. Hence, a meta-analysis was performed to assess the therapeutic effect of this treatment regimen.

Methods

Five English databases (PubMed, WOS, CENTRAL, Scopus, and Embase), and four Chinese databases (CBM, CNKI, VIP, and Wanfang), as well as oncological meetings, were systematically searched to identify eligible studies that assessed the addition of ICI to CT versus CT alone in metastatic triple-negative breast cancer. The pooled hazard ratios (HR) of progression-free survival (PFS) and overall survival (OS) were estimated using fixed- or random-effect model. Subgroup analyses were also performed in the intention-to-treat (ITT) and PD-L1-positive individuals.

Results

All told there are five eligible randomized controlled trials involving 3,000 patients were enrolled in this meta-analysis. Compared with CT alone, the ICI plus CT regimen significantly increased PFS in the ITT (HR = 0.80, 95% CI: 0.73-0.88) and PD-L1-positive (HR = 0.70, 95% CI: 0.62-0.79) populations, as well as OS in the ITT (HR = 0.89, 95% CI: 0.81-0.97) and PD-L1-positive populations (HR = 0.80, 95% CI: 0.71-0.91). Moreover, the PFS of sufferers treated with the combination strategy of ICI with CT increased alongside PD-L1 enrichment. A clinical benefit in terms of objective response rate was also distinctly observed in both populations treated with ICI plus CT. In the subgroup analysis, patients in the no prior CT subgroup experienced a striking increase in PFS in both populations; however, a difference was not observed in other subgroups.

Conclusions

The combination strategy striking improves PFS and OS in both ITT and PD-L1-positive populations, and PFS is prolonged with PD-L1 enrichment. Patients who do not receive CT prior to this treatment are associated with longer PFS in both populations.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/#recordDetails, identifier CRD42021289817.

Harmonization of tumor mutation burden testing with comprehensive genomic profiling assays: an IQN Path initiative

BACKGROUND

Although conflicting results emerged from different studies, the tumor mutational burden (TMB) appears as one of most reliable biomarkers of sensitivity to immune checkpoint inhibitors. Several laboratories are reporting TMB values when performing comprehensive genomic profiling (CGP) without providing a clinical interpretation, due to the lack of validated cut-off values. The International Quality Network for Pathology launched an initiative to harmonize TMB testing with CGP assay and favor the clinical implementation of this biomarker.

METHODS

TMB evaluation was performed with three commercially available CGP panels, TruSight Oncology 500 (TSO500), Oncomine Comprehensive Plus Assay (OCA) and QIAseq Multimodal Panel (QIA), versus the reference assay FoundationOne CDx (F1CDx). Archived clinical samples derived from 60 patients with non-small cell lung cancer were used for TMB assessment. Adjusted cut-off values for each panel were calculated.

RESULTS

Testing was successful for 91.7%, 100%, 96.7% and 100% of cases using F1CDx, TSO500, OCA and QIA, respectively. The matrix comparison analysis, between the F1CDx and CGP assays, showed a linear correlation for all three panels, with a higher correlation between F1CDx and TSO500 (rho=0.88) than in the other two comparisons (rho=0.77 for QIA; 0.72 for OCA). The TSO500 showed the best area under the curve (AUC, value 0.96), with a statistically significant difference when compared with the AUC of OCA (0.83, p value=0.01) and QIA (0.88, p value=0.028). The Youden Index calculation allowed us to extrapolate TMB cut-offs of the different panels corresponding to the 10 mutations/megabase (muts/Mb) cut-off of F1CDx: 10.19, 10.4 and 12.37 muts/Mb for TSO500, OCA and QIA, respectively. Using these values, we calculated the relative accuracy measures for the three panels. TSO500 showed 86% specificity and 96% sensitivity, while OCA and QIA had lower yet similar values of specificity and sensitivity (73% and 88%, respectively).

CONCLUSION

This study estimated TMB cut-off values for commercially available CGP panels. The results showed a good performance of all panels on clinical samples and the calculated cut-offs support better accuracy measures for TSO500. The validated cut-off values can drive clinical interpretation of TMB testing in clinical research and clinical practice.

The practical utility of AI-assisted molecular profiling in the diagnosis and management of cancer of unknown primary: an updated review

Cancer of unknown primary (CUP) presents a complex diagnostic challenge, characterized by metastatic tumors of unknown tissue origin and a dismal prognosis. This review delves into the emerging significance of artificial intelligence (AI) and machine learning (ML) in transforming the landscape of CUP diagnosis, classification, and treatment. ML approaches, trained on extensive molecular profiling data, have shown promise in accurately predicting tissue of origin. Genomic profiling, encompassing driver mutations and copy number variations, plays a pivotal role in CUP diagnosis by providing insights into tumor type-specific oncogenic alterations. Mutational signatures (MS), reflecting somatic mutation patterns, offer further insights into CUP diagnosis. Known MS with established etiology, such as ultraviolet (UV) light-induced DNA damage and tobacco exposure, have been identified in cases of dedifferentiated/transdifferentiated melanoma and carcinoma. Deep learning models that integrate gene expression data and DNA methylation patterns offer insights into tissue lineage and tumor classification. In digital pathology, machine learning algorithms analyze whole-slide images to aid in CUP classification. Finally, precision oncology, guided by molecular profiling, offers targeted therapies independent of primary tissue identification. Clinical trials assigning CUP patients to molecularly guided therapies, including targetable alterations and tumor mutation burden as an immunotherapy biomarker, have resulted in improved overall survival in a subset of patients. In conclusion, AI- and ML-driven approaches are revolutionizing CUP management by enhancing diagnostic accuracy. Precision oncology utilizing enhanced molecular profiling facilitates the identification of targeted therapies that transcend the need to identify the tissue of origin, ultimately improving patient outcomes.

Unravelling the heterogeneity of oral squamous cell carcinoma by integrative analysis of single-cell and bulk transcriptome data

Oral squamous cell carcinoma (OSCC) is a prevalent malignancy of the head and neck with rising global incidence. Despite advances in treatment modalities, OSCC prognosis remains diverse due to the complex molecular and cellular heterogeneity within tumours, as well as the heterogeneity in tumour microenvironment (TME). In this study, we utilized single-cell RNA sequencing (scRNA-seq) analysis to explore distinct subpopulations of tumour cells in OSCC tissues and their interaction with components in TME. We identified four major tumour cell subpopulations (C0, C1, C2 and C3) with unique molecular characteristics and functional features. Pathway enrichment analysis revealed that C0 primarily expressed genes involved in extracellular matrix interactions and C1 showed higher proliferation levels, suggesting that the two cell subpopulations exhibited tumour aggressiveness. Conversely, C2 and C3 displayed features associated with keratinization and cornified envelope formation. Accordingly, C0 and C1 subpopulations were associated with shorter overall and disease-free survival times, while C2 and C3 were weakly correlated with longer survival. Genomic analysis showed that C1 demonstrated a positive correlation with tumour mutation burden. Furthermore, C0 exhibited resistant to cisplatin treatment, while C1 showed more sensitive to cisplatin treatment, indicating that C0 might exhibit more aggressive compared to C1. Additionally, C0 had a higher level of communication with fibroblasts and endothelial cells in TME via integrin-MAPK signalling, suggesting that the function of C0 was maintained by that pathway. In summary, this study provided critical insights into the molecular and cellular heterogeneity of OSCC, with potential implications for prognosis prediction and personalized therapeutic approaches.

A comprehensive analysis of clinical and polygenic germline influences on somatic mutational burden

Tumor mutational burden (TMB), the total number of somatic mutations in the tumor, and copy number burden (CNB), the corresponding measure of aneuploidy, are established fundamental somatic features and emerging biomarkers for immunotherapy. However, the genetic and non-genetic influences on TMB/CNB and, critically, the manner by which they influence patient outcomes remain poorly understood. Here, we present a large germline-somatic study of TMB/CNB with >23,000 individuals across 17 cancer types, of which 12,000 also have extensive clinical, treatment, and overall survival (OS) measurements available. We report dozens of clinical associations with TMB/CNB, observing older age and male sex to have a strong effect on TMB and weaker impact on CNB. We additionally identified significant germline influences on TMB/CNB, including fine-scale European ancestry and germline polygenic risk scores (PRSs) for smoking, tanning, white blood cell counts, and educational attainment. We quantify the causal effect of exposures on somatic mutational processes using Mendelian randomization. Many of the identified features associated with TMB/CNB were additionally associated with OS for individuals treated at a single tertiary cancer center. For individuals receiving immunotherapy, we observed a complex relationship between PRSs for educational attainment, self-reported college attainment, TMB, and survival, suggesting that the influence of this biomarker may be substantially modified by socioeconomic status. While the accumulation of somatic alterations is a stochastic process, our work demonstrates that it can be shaped by host characteristics including germline genetics.

Molecular pathology as basis for timely cancer diagnosis and therapy

Precision and personalized therapeutics have witnessed significant advancements in technology, revolutionizing the capabilities of laboratories to generate vast amounts of genetic data. Coupled with computational resources for analysis and interpretation, and integrated with various other types of data, including genomic data, electronic medical health (EMH) data, and clinical knowledge, these advancements support optimized health decisions. Among these technologies, next-generation sequencing (NGS) stands out as a transformative tool in the field of cancer treatment, playing a crucial role in precision oncology. NGS-based workflows are employed across a range of applications, including gene panels, exome sequencing, and whole-genome sequencing, supporting comprehensive analysis of the entire cancer genome, including mutations, copy number variations, gene expression profiles, and epigenetic modifications. By utilizing the power of NGS, these workflows contribute to enhancing our understanding of disease mechanisms, diagnosis confirmation, identifying therapeutic targets, and guiding personalized treatment decisions. This manuscript explores the diverse applications of NGS in cancer treatment, highlighting its significance in guiding diagnosis and treatment decisions, identifying therapeutic targets, monitoring disease progression, and improving patient outcomes.

G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma

BACKGROUND

Liver Hepatocellular carcinoma (LIHC) exhibits a high incidence of liver cancer with escalating mortality rates over time. Despite this, the underlying pathogenic mechanism of LIHC remains poorly understood.

MATERIALS & METHODS

To address this gap, we conducted a comprehensive investigation into the role of G6PD in LIHC using a combination of bioinformatics analysis with database data and rigorous cell experiments. LIHC samples were obtained from TCGA, ICGC and GEO databases, and the differences in G6PD expression in different tissues were investigated by differential expression analysis, followed by the establishment of Nomogram to determine the percentage of G6PD in causing LIHC by examining the relationship between G6PD and clinical features, and the subsequent validation of the effect of G6PD on the activity, migration, and invasive ability of hepatocellular carcinoma cells by using the low expression of LI-7 and SNU-449. Additionally, we employed machine learning to validate and compare the predictive capacity of four algorithms for LIHC patient prognosis.

RESULTS

Our findings revealed significantly elevated G6PD expression levels in liver cancer tissues as compared to normal tissues. Meanwhile, Nomogram and Adaboost, Catboost, and Gbdt Regression analyses showed that G6PD accounted for 46%, 31%, and 49% of the multiple factors leading to LIHC. Furthermore, we observed that G6PD knockdown in hepatocellular carcinoma cells led to reduced proliferation, migration, and invasion abilities. Remarkably, the Decision Tree C5.0 decision tree algorithm demonstrated superior discriminatory performance among the machine learning methods assessed.

CONCLUSION

The potential diagnostic utility of G6PD and Decision Tree C5.0 for LIHC opens up a novel avenue for early detection and improved treatment strategies for hepatocellular carcinoma.

Tissue and circulating biomarkers of benefit to immunotherapy in extensive-stage small cell lung cancer patients

Extensive stage-Small-Cell Lung Cancer (ES-SCLC) is an aggressive cancer with dismal prognosis. The addition of immune-checkpoint inhibitors (ICIs) to platinum-based chemotherapy have been consistently demonstrated to improve outcomes and survival, becoming the new standard in first - line treatment of ES-SCLC patients. However, despite positive results reported in the pivotal trials, longer benefit appears evident only for a selected group of patients. Several predictive biomarkers have been studied so far but the prospective identification of patients more likely to experience better outcome seems to be challenging in SCLC. Indeed, classical immune predictive biomarkers as PD-L1 and tumor mutational burden (TMB) seem not to correlate with outcomes. Recently, a new molecular classification of SCLC based on differential expression of genes associated with specific clinical behaviors and therapeutic vulnerability have been presented suggesting a new field to be investigated. Despite the achievements, these studies focused mainly on inter-tumoral heterogeneity, limiting the exploration of intra-tumoral heterogeneity and cell to cell interactions. New analysis methods are ongoing in order to explore subtypes plasticity. Analysis on single biopsies cannot catch the whole genomic profile and dynamic change of disease over time and during treatment. Moreover, the availability of tissue for translational research is limited due to the low proportion of patients undergoing surgery. In this context, liquid biopsy is a promising tool to detect reliable predictive biomarkers. Here, we reviewed the current available data on predictive role of tissue and liquid biomarkers in ES-SCLC patients receiving ICIs. We assessed latest results in terms of predictive and prognostic value of gene expression profiling in SCLC. Finally, we explored the role of liquid biopsy as a tool to monitor SCLC patients over time.

A novel PANoptosis-related long non-coding RNA index to predict prognosis, immune microenvironment and personalised treatment in hepatocellular carcinoma

BACKGROUND

PANoptosis is involved in the interaction of apoptosis, necroptosis and pyroptosis, playing a role in programmed cell death. Moreover, long non-coding RNAs (lncRNAs) regulate the PCD. This work aims to explore the role of PANoptosis-associated lncRNAs in hepatocellular carcinoma (HCC).

METHODS

Co-expression analysis identified PANoptosis-associated lncRNAs in HCC. Cox and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were utilised to filter lncRNAs and establish a PANoptosis-related lncRNA index (PANRI). Additionally, Cox, Kaplan-Meier and receiver operating characteristic (ROC) curves were utilised to systematically evaluate the PANRI. Furthermore, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), single sample gene set enrichment analysis (ssGSEA) and immune checkpoints were performed to analyse the potential of the PANRI in differentiating different tumour immune microenvironment (TIME) populations. The consensus clustering algorithm was used to distinguish individuals with HCC having different TIME subtypes. Finally, HCC cell lines HepG2 were utilised for further validation in in vitro experiments.

RESULTS

The PANRI differentiates patients according to risk. Notably, ESTIMATE and ssGSEA algorithms revealed a high immune infiltration status in high-risk patients. Additionally, consensus clustering divided the patients into three clusters to identify different subtypes of TIME. Moreover, in vitro results showed that siRNA-mediated silencing of AL049840.4 inhibited the viability and migration of HepG2 cells and promoted apoptosis.

CONCLUSIONS

This is the first PANoptosis-related, lncRNA-based risk index in HCC to assess patient prognosis, TIME and response to immunotherapy. This study offers novel perspectives on the role of PANoptosis-associated lncRNAs in HCC.

Genomic Features of Homologous Recombination Deficiency in Breast Cancer: Impact on Testing and Immunotherapy

Genomic instability is one of the well-established hallmarks of cancer. The homologous recombination repair (HRR) pathway plays a critical role in correcting the double-stranded breaks (DSB) due to DNA damage in human cells. Traditionally, the BRCA1/2 genes in the HRR pathway have been tested for their association with breast cancer. However, defects in the HRR pathway (HRD, also termed 'BRCAness'), which has up to 50 genes, have been shown to be involved in tumorigenesis and treatment susceptibility to poly-ADP ribose polymerase inhibitors (PARPis), platinum-based chemotherapy, and immune checkpoint inhibitors (ICIs). A reliable consensus on HRD scores is yet to be established. Emerging evidence suggests that only a subset of breast cancer patients benefit from ICI-based immunotherapy. Currently, albeit with limitations, the expression of programmed death-ligand 1 (PDL1) and tumor mutational burden (TMB) are utilized as biomarkers to predict the favorable outcomes of ICI therapy in breast cancer patients. Preclinical studies demonstrate an interplay between the HRR pathway and PDL1 expression. In this review, we outline the current understanding of the role of HRD in genomic instability leading to breast tumorigenesis and delineate outcomes from various clinical trials. Furthermore, we discuss potential strategies for combining HRD-targeted therapy with immunotherapy to achieve the best healthcare outcomes in breast cancer patients.

Recombinant Newcastle disease viruses expressing immunological checkpoint inhibitors induce a pro-inflammatory state and enhance tumor-specific immune responses in two murine models of cancer

Introduction

Tumor microenvironments are immunosuppressive due to progressive accumulation of mutations in cancer cells that can drive expression of a range of inhibitory ligands and cytokines, and recruitment of immunomodulatory cells, including myeloid-derived suppressor cells (MDSC), tumor-associated macrophages, and regulatory T cells (Tregs).

Methods

To reverse this immunosuppression, we engineered mesogenic Newcastle disease virus (NDV) to express immunological checkpoint inhibitors anti-cytotoxic T lymphocyte antigen-4 and soluble programmed death protein-1.

Results

Intratumoral administration of recombinant NDV (rNDV) to mice bearing intradermal B16-F10 melanomas or subcutaneous CT26LacZ colon carcinomas led to significant changes in the tumor-infiltrating lymphocyte profiles. Vectorizing immunological checkpoint inhibitors in NDV increased activation of intratumoral natural killer cells and cytotoxic T cells and decreased Tregs and MDSCs, suggesting induction of a pro-inflammatory state with greater infiltration of activated CD8+ T cells. These notable changes translated to higher ratios of activated effector/suppressor tumor-infiltrating lymphocytes in both cancer models, which is a promising prognostic marker. Whereas all rNDV-treated groups showed evidence of tumor regression and increased survival in the CT26LacZ and B16-F10, only treatment with NDV expressing immunological checkpoint blockades led to complete responses compared to tumors treated with NDV only.

Discussion

These data demonstrated that NDV expressing immunological checkpoint inhibitors could reverse the immunosuppressive state of tumor microenvironments and enhance tumor-specific T cell responses.

Genetic insights into carbohydrate sulfotransferase 8 and its impact on the immunotherapy efficacy of cancer

Immune checkpoint blockade (ICB) is a promising therapy for solid tumors, but its effectiveness depends on biomarkers that are not precise. Here, we utilized genome-wide association study to investigate the association between genetic variants and tumor mutation burden to interpret ICB response. We identified 16 variants (p < 5 × 10-8) probed to 17 genes on 9 chromosomes. Subsequent analysis of one of the most significant loci in 19q13.11 suggested that the rs111308825 locus at the enhancer is causal, as its A allele impairs KLF2 binding, leading to lower carbohydrate sulfotransferase 8 (CHST8) expression. Breast cancer cells expressing CHST8 suppress T cell activation, and Chst8 loss attenuates tumor growth in a syngeneic mouse model. Further investigation revealed that programmed death-ligand 1 (PD-L1) and its homologs could be sulfated by CHST8, resulting in M2-like macrophage enrichment in the tumor microenvironment. Finally, we confirmed that low-CHST8 tumors have better ICB response, supporting the genetic effect and clinical value of rs111308825 for ICB efficacy prediction.

Enhancing the quality of panel-based tumor mutation burden assessment: a comprehensive study of real-world and in-silico outcomes

Targeted panel-based tumor mutation burden (TMB) assays are widely employed to guide immunotherapy for patients with solid tumors. However, the accuracy and consistency of this method can be compromised due to the variability in technical details across different laboratories, particularly in terms of panel size, somatic mutation detection and TMB calculation rules. Currently, systematic evaluations of the impact of these technical factors on existing assays and best practice recommendations remain lacking. We assessed the performance of 50 participating panel-based TMB assays involving 38 unique methods using cell line samples. In silico experiments utilizing TCGA MC3 datasets were performed to further dissect the impact of technical factors. Here we show that the panel sizes beyond 1.04 Mb and 389 genes are necessary for the basic discrete accuracy, as determined by over 40,000 synthetic panels. The somatic mutation detection should maintain a reciprocal gap of recall and precision less than 0.179 for reliable psTMB calculation results. The inclusion of synonymous, nonsense and hotspot mutations could enhance the accuracy of panel-based TMB assay. A 5% variant allele frequency cut-off is suitable for TMB assays using tumor samples with at least 20% tumor purity. In conclusion, this multicenter study elucidates the major technical factors as sources of variability in panel-based TMB assays and proposed comprehensive recommendations for the enhancement of accuracy and consistency. These findings will assist clinical laboratories in optimizing the methodological details through bioinformatic experiments to enhance the reliability of panel-based methods.

Identification and validation of SLCO4C1 as a biological marker in hepatocellular carcinoma based on anoikis classification features

BACKGROUND

Hepatocellular carcinoma (HCC) exhibits a high degree of invasiveness and is closely associated with rapid disease progression. Multiple lines of evidence indicate a strong correlation between anoikis resistance and tumor progression, invasion, and metastasis. Nevertheless, the classification of anoikis in HCC and the investigation of novel biological target mechanisms in this context continue to pose challenges, requiring further exploration.

METHODS

Combined with HCC samples from TCGA, GEO and ICGC databases, cluster analysis was conducted on anoikis genes, revealing novel patterns among different subtypes. Significant gene analysis of different gene subtypes was performed using WCGNA. The anoikis prognostic risk model was established by Lasso-Cox. Go, KEGG, and GSEA were applied to investigate pathway enrichment primarily observed in risk groups. We compared the disparities in immune infiltration, TMB, tumor microenvironment (TME), and drug sensitivity between the two risk groups. RT-qPCR and Western blotting were performed to validate the expression levels of SLCO4C1 in HCC. The biological functions of SLCO4C1 in HCC cells were assessed through various experiments, including CCK8 assay, colony formation assay, invasion migration assay, wound healing assay, and flow cytometry analysis.

RESULTS

HCC was divided into 2 anoikis subtypes, and the subtypeB had a better prognosis. An anoikis prognostic model based on 12 (COPZ2, ACTG2, IFI27, SPP1, EPO, SLCO4C1, RAB26, STC2, RAC3, NQO1, MYCN, HSPA1B) risk genes is important for survival and prognosis. Significant differences were observed in immune cell infiltration, TME, and drug sensitivity analysis between the risk groups. SLCO4C1 was downregulated in HCC. SLCO4C1 downregulation promoted the proliferation, invasion, migration, and apoptosis of HCC cells. The tumor-suppressive role of SLCO4C1 in HCC has been confirmed.

CONCLUSIONS

Our study presents a novel anoikis classification method for HCC that reveals the association between anoikis features and HCC. The anoikis feature is a critical biomarker bridging tumor cell death and tumor immunity. In this study, we provided the first evidence of SLCO4C1 functioning as a tumor suppressor in HCC.

Melanoma and microbiota: Current understanding and future directions

Over the last decade, the composition of the gut microbiota has been found to correlate with the outcomes of cancer patients treated with immunotherapy. Accumulating evidence points to the various mechanisms by which intestinal bacteria act on distal tumors and how to harness this complex ecosystem to circumvent primary resistance to immune checkpoint inhibitors. Here, we review the state of the microbiota field in the context of melanoma, the recent breakthroughs in defining microbial modes of action, and how to modulate the microbiota to enhance response to cancer immunotherapy. The host-microbe interaction may be deciphered by the use of "omics" technologies, and will guide patient stratification and the development of microbiota-centered interventions. Efforts needed to advance the field and current gaps of knowledge are also discussed.

Clinical Utility and Benefits of Comprehensive Genomic Profiling in Cancer

BACKGROUND

Comprehensive genomic profiling (CGP) with next-generation sequencing detects genetic alterations of hundreds of genes simultaneously and multiple molecular biomarkers with one test. In the personalized medicine era, CGP is increasingly used for cancer diagnosis, treatment selection, and prognosis prediction.

CONTENT

In this review, we summarize the benefits of CGP, clinical utility of CGP, and challenges of setting up CGP in the clinical laboratories. Besides the genetic alterations identified in the cancer-related genes, other biomarkers such as tumor mutational burden, microsatellite instability, and homologous recombination deficiency are critical for initiating targeted therapy. Compared with conventional tests, CGP uses less specimen and shortens the turnaround time if multiple biomarkers need to be tested. RNA fusion assay and liquid biopsy are helpful additions to DNA-based CGP by detecting fusions/splicing variants and complementing tissue-based CGP findings, respectively.

SUMMARY

Many previous hurdles for implementing CGP in the clinical laboratories have been gradually alleviated such as the decrease in sequencing cost, availability of both open-source and commercial bioinformatics tools, and improved reimbursement. These changes have helped to make CGP available to a greater population of cancer patients for improving characterization of their tumors and expanding their eligibility for clinical trials. Additionally, sequencing results of the hundreds of genes on CGP panels could be further analyzed to better understand the biology of various cancers and identify new biomarkers.

Identification and verification of PTPN3 as a novel biomarker in predicting cancer prognosis, immunity, and immunotherapeutic efficacy

BACKGROUND

The importance of protein tyrosine phosphatase non-receptor type 3 (PTPN3) in controlling multifaceted tumor cell behaviors throughout cancer development has received widespread attention. Nevertheless, little is known about the biological roles of PTPN3 in drug sensitivity, immunotherapeutic effectiveness, tumor immune microenvironment, and cancer prognosis.

METHODS

The Cancer Genome Atlas (TCGA) database's RNAseq data were used to examine the expression of PTPN3 in 33 different cancer types. In addition, immunohistochemistry (IHC) was performed to validate the expression of PTPN3 across various cancer types within our clinical cohorts. The features of PTPN3 alterations were demonstrated throughout the cBioPortal database. This study focused on examining the prognostic and clinicopathological importance of PTPN3 through the acquisition of clinical data from the TCGA database. The investigation of PTPN3's probable role in the tumor immune microenvironment was demonstrated by the application of CIBERSORT, ESTIMATE algorithms, and the TISIDB database. Using Spearman's rank correlation coefficient, the relationships between PTPN3 expression and tumor mutation burden (TMB) and microsatellite instability (MSI) were evaluated. To further investigate the putative biological activities and downstream pathways of PTPN3 in various cancers in humans, Gene Set Enrichment Analysis (GSEA) was carried out. In addition, an examination was conducted to explore the associations between PTPN3 and the effectiveness of PD-1/PD-L1 inhibitors, utilizing data extracted from the GEO database.

RESULTS

PTPN3 was abnormally expressed in multiple cancer types and was also strictly associated with the prognosis of cancer patients. IHC was used to investigate and confirm the various expression levels of PTPN3 in various malignancies, including breast cancer, lung cancer, sarcoma, and kidney renal clear cell carcinoma in our clinical cohorts. There is a high correlation between the levels of PTPN3 expression in different cancers and infiltrating immune cells, including mast cells, B cells, regulatory T cells, CD8 + T cells, macrophages, and dendritic cells. Infiltrating immune cells, such as regulatory T cells, CD8 + T cells, macrophages, B cells, dendritic cells, and mast cells, are strongly correlated with PTPN3 expression levels in various tumors. The expression of PTPN3 exhibited a substantial correlation with many immune-related biomolecules and the expression of TMB and MSI in multiple types of cancer. In addition, PTPN3 has demonstrated promise in predicting the therapeutic benefits of PD-1/PD-L1 inhibitors and the susceptibility to anti-cancer medications in the treatment of clinical cancer.

CONCLUSIONS

Our findings highlight the importance of PTPN3 as a prognostic biomarker and predictor of immunotherapy success in various forms of cancer. Furthermore, PTPN3 appears to have an important role in modifying the tumor immune microenvironment, highlighting its potential as a promising biomarker for prognosis prediction, immunotherapeutic efficacy evaluation, and identification of immune-related characteristics in diverse cancer types.

Methods for Estimating Personal Disease Risk and Phylogenetic Diversity of Hematopoietic Stem Cells

An individual's chronological age does not always correspond to the health of different tissues in their body, especially in cases of disease. Therefore, estimating and contrasting the physiological age of tissues with an individual's chronological age may be a useful tool to diagnose disease and its progression. In this study, we present novel metrics to quantify the loss of phylogenetic diversity in hematopoietic stem cells (HSCs), which are precursors to most blood cell types and are associated with many blood-related diseases. These metrics showed an excellent correspondence with an age-related increase in blood cancer incidence, enabling a model to estimate the phylogeny-derived age (phyloAge) of HSCs present in an individual. The HSC phyloAge was generally older than the chronological age of patients suffering from myeloproliferative neoplasms (MPNs). We present a model that relates excess HSC aging with increased MPN risk. It predicted an over 200 times greater risk based on the HSC phylogenies of the youngest MPN patients analyzed. Our new metrics are designed to be robust to sampling biases and do not rely on prior knowledge of driver mutations or physiological assessments. Consequently, they complement conventional biomarker-based methods to estimate physiological age and disease risk.

Integrated analysis of FHIT gene alterations in cancer

The Fragile Histidine Triad Diadenosine Triphosphatase (FHIT) gene is located in the Common Fragile Site FRA3B and encodes an enzyme that hydrolyzes the dinucleotide Ap3A. Although FHIT loss is one of the most frequent copy number alterations in cancer, its relevance for cancer initiation and progression remains unclear. FHIT is frequently lost in cancers from the digestive tract, which is compatible with being a cancer driver event in these tissues. However, FHIT loss could also be a passenger event due to the inherent fragility of the FRA3B locus. Moreover, the physiological relevance of FHIT enzymatic activity and the levels of Ap3A is largely unclear. We have conducted here a systematic pan-cancer analysis of FHIT status in connection with other mutations and phenotypic alterations, and we have critically discussed our findings in connection with the literature to provide an overall view of FHIT implications in cancer.

Molecular heterogeneity in prostate cancer and the role of targeted therapy

Data collected from large-scale studies has shown that the incidence of prostate cancer globally is on the rise, which could be attributed to an overall increase in lifespan. So, the question is how has modern science with all its new technologies and clinical breakthroughs mitigated or managed this disease? The answer is not a simple one as prostate cancer exhibits various subtypes, each with its unique characteristics or signatures which creates challenges in treatment. To understand the complexity of prostate cancer these signatures must be deciphered. Molecular studies of prostate cancer samples have identified certain genetic and epigenetic alterations, which are instrumental in tumorigenesis. Some of these candidates include the androgen receptor (AR), various oncogenes, tumor suppressor genes, and the tumor microenvironment, which serve as major drivers that lead to cancer progression. These aberrant genes and their products can give an insight into prostate cancer development and progression by acting as potent markers to guide future therapeutic approaches. Thus, understanding the complexity of prostate cancer is crucial for targeting specific markers and tailoring treatments accordingly.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Comprehensive Pan-Cancer Mutation Density Patterns in Enhancer RNA

Significant advances have been achieved in understanding the critical role of enhancer RNAs (eRNAs) in the complex field of gene regulation. However, notable uncertainty remains concerning the biology of eRNAs, highlighting the need for continued research to uncover their exact functions in cellular processes and diseases. We present a comprehensive study to scrutinize mutation density patterns, mutation strand bias, and mutation burden in eRNAs across multiple cancer types. Our findings reveal that eRNAs exhibit mutation strand bias akin to that observed in protein-coding RNAs. We also identified a novel pattern, in which mutation density is notably diminished around the central region of the eRNA, but conspicuously elevated towards both the beginning and end. This pattern can be potentially explained by a mechanism involving heightened transcriptional activity and the activation of transcription-coupled repair. The central regions of the eRNAs appear to be more conserved, hinting at a potential mechanism preserving their structural and functional integrity, while the extremities may be more susceptible to mutations due to increased exposure. The evolutionary trajectory of this mutational pattern suggests a nuanced adaptation in eRNAs, where stability at their core coexists with flexibility at their extremities, potentially facilitating their diverse interactions with other genetic entities.

Comprehensively prognostic and immunological analysis of VRK Serine/Threonine Kinase 1 in pan-cancer and identification in hepatocellular carcinoma

BACKGROUND

VRK1 is a member of the vaccinia-related kinase (VRK) family of serine/threonine protein kinases, which is related to the occurrence and development of malignant tumors. The expression pattern, predictive value, and biological function of VRK1 in various cancers remain largely elusive and warrant further investigation.

METHODS

Public databases, such as TCGA, GTEx, and UCEC, were utilized to comprehensively analyze the expression of VRK1 across multiple cancer types. Prognostic significance was assessed through Univariate Cox regression and Kaplan-Meier analyses. Additionally, Spearman's correlation analysis was employed to explore the potential associations between VRK1 expression and various factors, including tumor microenvironment scores, immune cell infiltration, and immune-related genes. Moreover, to validate the findings, differential expression of VRK1 in HCC tissues and cell lines was further confirmed using qPCR, Western blot, and immunohistochemistry techniques.

RESULTS

The upregulation of VRK1 was observed in most cancer types, and was associated with worse prognosis in ACC, KICH, KIRP, LGG, LIHC, LUAD, MESO, and PCPG. In various cancers, VRK1 expression exhibited positive correlations with immune infiltrating cells, immune checkpoint-related genes, TMB, and MSI. Furthermore, the promoter methylation status of VRK1 varied across different tumor types, and this variation was associated with patient prognosis in certain cancers. In our experimental analyses, we observed significantly elevated expression of VRK1 in both HCC tissues and HCC cells. Functionally, we found that the downregulation of VRK1 had a profound impact on HCC cells, leading to a significant decrease in their proliferation, migration, and invasion capabilities.

CONCLUSION

The expression of VRK1 exerts a notable influence on the prognosis of several tumors and exhibits a strong correlation with tumor immune infiltration. Moreover, in the context of HCC, VRK1 may act as an oncogene, actively promoting tumor progression.

Circulating tumor DNA: current implementation issues and future challenges for clinical utility

Over the past decades, liquid biopsy, especially circulating tumor DNA (ctDNA), has received tremendous attention as a noninvasive detection approach for clinical applications, including early diagnosis of cancer and relapse, real-time therapeutic efficacy monitoring, potential target selection and investigation of drug resistance mechanisms. In recent years, the application of next-generation sequencing technology combined with AI technology has significantly improved the accuracy and sensitivity of liquid biopsy, enhancing its potential in solid tumors. However, the increasing integration of such promising tests to improve therapy decision making by oncologists still has complexities and challenges. Here, we propose a conceptual framework of ctDNA technologies and clinical utilities based on bibliometrics and highlight current challenges and future directions, especially in clinical applications such as early detection, minimal residual disease detection, targeted therapy, and immunotherapy. We also discuss the necessities of developing a dynamic field of translational cancer research and rigorous clinical studies that may support therapeutic strategy decision making in the near future.

The inter-link of ageing, cancer and immunity: findings from real-world retrospective study

BACKGROUND

Although the concept of declined immune function associated with cancer has been accepted extensively, real-world clinical studies focusing on analysis of the peripheral blood immune changes underlying ageing, immunity and cancer are scarce.

METHODS

In this case-control study, we retrospectively analysed 1375 cancer patients and enrolled 275 age and gender matched healthy individuals. Flow cytometry was conducted to assess the immune changes. Further analysis was examined by SPSS 17.0 and GraphPad Prism 9 software.

RESULTS

Cancer patients showed obviously decreased CD3+ T, CD3+CD4+ Th, CD3+CD8+ CTL, CD19+ B, CD16+CD56+ NK cell counts and lower percentage of PD-1 (programmed cell death protein-1, PD-1) positive cells than healthy control (P < 0.0001). For cancer patients, the reference range of circulating percentage of PD-1+CD45+ cells, PD-1+CD3+ T cells, PD-1+CD3+CD4+ Th cells and PD-1+CD3+CD8+ CTL (Cytotoxic T Lymphocyte, CTL) were 11.2% (95% CI 10.8%-11.6%), 15.5% (95% CI 14.7%-16.0%), 15.4% (95% CI 14.9%-16.0%) and 14.5% (95% CI 14.0%-15.5%), respectively. Moreover, the reduction of CD3+ T, CD3+CD4+ Th, CD3+CD8+ CTL, CD19+ B cell counts accompanied with age and stage advancing (P < 0.05). CD16+CD56+ NK cells decreased with stage, but elevated in aged and male cancer patients (P < 0.05). Additionally, the percentage of PD-1 positive cells varied across cancer types, raised with age and stage. Head and neck, pancreatic, gynaecological and lung demonstrated a higher level of the percentage of PD-1 positive cells than melanoma, prostate, and breast cancer (P < 0.05).

CONCLUSIONS

This study provides the reference range of the percentage of PD-1 positive cells on peripheral blood, confirms the decreased immune cells and a series of immune changes accompanying with cancer, expands our real world evidence to better understand the interactions of ageing, cancer and immunity. Moreover, the circulating percentage of PD-1 positive cells shows similar tumor type distribution with tumor mutational burden (TMB), supports that it maybe a potential predictive biomarker for immune checkpoint inhibitor therapy.

Challenges and Future Directions in the Management of Tumor Mutational Burden-High (TMB-H) Advanced Solid Malignancies

A standardized assessment of Tumor Mutational Burden (TMB) poses challenges across diverse tumor histologies, treatment modalities, and testing platforms, requiring careful consideration to ensure consistency and reproducibility. Despite clinical trials demonstrating favorable responses to immune checkpoint inhibitors (ICIs), not all patients with elevated TMB exhibit benefits, and certain tumors with a normal TMB may respond to ICIs. Therefore, a comprehensive understanding of the intricate interplay between TMB and the tumor microenvironment, as well as genomic features, is crucial to refine its predictive value. Bioinformatics advancements hold potential to improve the precision and cost-effectiveness of TMB assessments, addressing existing challenges. Similarly, integrating TMB with other biomarkers and employing comprehensive, multiomics approaches could further enhance its predictive value. Ongoing collaborative endeavors in research, standardization, and clinical validation are pivotal in harnessing the full potential of TMB as a biomarker in the clinic settings.

m6A and m5C modification of GPX4 facilitates anticancer immunity via STING activation

Cancer immunotherapy is arguably the most rapidly advancing realm of cancer treatment. Glutathione peroxidase 4 (GPX4) has emerged as the vital enzyme to prevent lipid peroxidation and maintain cellular redox homeostasis. However, the mechanism of GPX4 in the regulation of cancer immunotherapy of colon adenocarcinoma (COAD) are incompletely understood. In pan-cancer analysis, we found that GPX4 showed remarkably upregulated expression and exhibited significant association with overall survival in multiple cancer types, especially COAD. Furthermore, upregulated GPX4 expression was positively correlated with increased immune cells infiltration and enhanced expression of immunomodulators. Mechanistically, RBM15B- and IGFBP2-mediated N6-methyladenosine (m6A) modification and NSUN5-mediated 5-methylcytosine (m5C) modification of GPX4 facilitated anticancer immunity via activation of cyclic GMP-AMP synthase (cGAS)-stimulator of interferon (STING) signaling by maintaining redox homeostasis in COAD. The risk model and nomogram model constructed based on the GPX4-derived genes further confirmed the prognostic and treatment-guiding value of GPX4. In all, our study demonstrated that m6A and m5C modification of GPX4 may be a promising target for cancer immunotherapy via activating the cGAS-STING signaling pathway in COAD.

Overexpression of MTHFD2 represents an inflamed tumor microenvironment and precisely predicts the molecular subtype and immunotherapy response of bladder cancer

Introduction

Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), whose aberrant expression is common in cancers, has recently been identified as a potential regulator of immune response. However, its immune-related role in bladder cancer (BLCA) and its association with immunotherapy efficacy remain unclear.

Methods

RNA sequencing data from The Cancer Genome Atlas (TCGA) was applied to analyze the immunological roles and prognostic value of MTHFD2 in pan-cancers. The association of MTHFD2 with several immunological features of tumor microenvironment (TME), including cancer-immunity cycle, immune cells infiltration, immune checkpoints expression, and T cell inflamed score was analyzed in TCGA-BLCA cohort. The predictors of cancer treatments effectiveness, including the expression and mutation of certain genes, molecular subtypes, and several signatures were evaluated as well. These results were validated by another independent cohort (GSE48075). Finally, the predictive value of MTHFD2 for TME and immunotherapy efficacy were validated using immunohistochemistry assay and RNA sequencing data from IMvigor210 cohort, respectively.

Results

MTHFD2 was found to be positively associated with several immunological features of an inflamed tumor microenvironment (TME) in various cancers and could predict BLCA patients' prognosis. In BLCA, high expression of MTHFD2 was observed to be positively related with the cancer-immunity cycle, the infiltration of several immune cells, and the expression of immunoregulators and T-cell inflamed scores, indicating a positive correlation with the inflamed TME. Moreover, patients with high MTHFD2 expression were more likely to be basal-like subtypes and respond to BLCA treatments, including immunotherapy, chemotherapy, and target therapy. The clinical data of the IMvigor210 cohort confirmed the higher response rates and better survival benefits of immunotherapy in high-MTHFD2-expression patients.

Conclusion

Collectively, high MTHFD2 predicts an inflamed TME, a basal-like subtype, and a better response to various therapeutic strategies, especially the ICB therapy, in bladder cancer.

Genetic and clinical landscape of ER + /PR- breast cancer in China

BACKGROUND

Estrogen receptor-positive and progesterone receptor-negative (ER + /PR-) breast cancer comprise a special type. More than 10% breast cancer patients belonged to ER + /PR-.

METHODS

In order to better understand this patient population, we utilized a unique dataset from China, examining the clinicopathological features and genomic profiles of ER + /PR- breast cancers. Our study involved three cohorts: Cohort 1 included 2120 unselected ER-positive female patients with re-evaluated clinicopathological and survival data; Cohort 2 comprised 442 ER-positive females who underwent genetic testing; and Cohort 3 consisted of 77 ER-positive/HER2-negative females tested with MammaPrint and BluePrint.

RESULTS

Patients were stratified into four categories based on the PR/ER ratio. Clinically, ER + /PR- tumors (PR/ER ratio = 0) showed the lowest proportion of T1 tumors (10.88%) and highest proportion of HER2-positive tumors (28.36%) than did other ER + /PR + tumors groups. The ER + /PR- group contained a higher number of underweight patients (20.20%). Independently of HER2 status, ER + /PR- patients demonstrated the poorest prognosis. Genomically, the most prevalent mutations were PIK3CA (50%) in ER + /PR + tumors and TP53 (65%) in ER + /PR- tumors. ER + /PR- tumors presented more frequent mutations in TP53, ERBB2, CDK12, SPEN, and NEB, with mutation rates of 65%, 42%, 27%, 13%, and 10%, respectively. Additionally, the Tumor Mutational Burden (TMB) was higher in the ER + /PR- group compared to the ER + /PR + group. The MammaPrint score for the ER + /PR-/HER2- group was significantly lower than that of other groups. In the BluePrint analysis, only four patients were classified as Basal-Type, all of whom were ER + /PR-/HER2-.

CONCLUSIONS

In this study, we identified the clinical and genetic characteristics of ER + /PR- breast cancer patients in China. Distinct PR statuses indicated different biological processes of ER + breast cancer and survival outcomes. Future treatment strategies may need to be tailored for ER + /PR- patients.

Tumor mutation burden in the prognosis and response of lung cancer patients to immune-checkpoint inhibition therapies

Immune checkpoint inhibition (ICI) therapies have reshaped the therapeutic landscape in lung cancer management, providing first-time improvements in patient response, prognosis, and overall survival. Despite their clinical effectiveness, variability in treatment responsiveness, as well as drug resistance, have led to a compelling need for predictive biomarkers facilitating the individualized selection of the most efficient therapeutic approach. Significant progress has been made in the identification of such biomarkers, with tumor mutation burden (ΤΜΒ) appearing as the leading and most promising predictive biomarker for the efficacy of ICIs in non-small cell lung cancer (NSCLC) among other tumors. Anti-PD-1/PD-L1 and anti-CTLA-4 antibodies have been extensively studied and clinically utilized. However, the overall efficiency of these drugs remains unsatisfactory, urging for the investigation of novel inhibitors, such as those targeting LAG-3, TIM-3, TIGIT and VISTA, which could be used either as a monotherapy or synergistically with the PD-1/PD-L1 or CTLA-4 blockers. Here, we investigate the role of TMB and cancer neoantigens as predictive biomarkers in the response of lung cancer patients to different ICI therapies, specifically focusing on the most recent immune checkpoint inhibitors, against LAG-3, TIM-3, TIGIT and VISTA. We further discuss the new trends in immunotherapies, including CAR T-cell therapy and personalized tumor vaccines. We also review further potential biomarkers that could be used in lung cancer response to immunotherapy, such as PD-L1+ IHC, MSI/dMMR, tumor infiltrating lymphocytes (TILs), as well as the role of the microbiome and circulating tumor DNA (ctDNA). Finally, we discuss the limitations and challenges of each.

Neoadjuvant Immune Checkpoint Inhibitor Therapy for Localized Deficient Mismatch Repair Colorectal Cancer: A Review

Importance

Colorectal cancers (CRCs) with deficient DNA mismatch repair (dMMR) account for 15% of all CRCs. Deficient MMR is a predictive biomarker associated with responsiveness to immune checkpoint inhibitors (ICIs) in solid tumors, including CRC. The remarkable effectiveness of ICIs in metastatic CRC has led to their evaluation in the neoadjuvant and adjuvant treatment of localized disease.

Observations

Multiple prospective phase 2 studies in limited numbers of patients with localized dMMR CRC demonstrate high complete clinical and pathological response rates (60%-100%) to neoadjuvant ICIs, with low rates of grade 3 or higher ICI-related toxic effects. Given the median follow-up of 12 to 25 months in these studies, longer-term monitoring is needed to determine the durability of response and to ensure that oncologic outcomes are not compromised in patients undergoing nonoperative management. Neoadjuvant ICI therapy is especially attractive for patients with rectal cancer given the significant morbidity that accompanies pelvic irradiation and total mesorectal excision. Ongoing and planned prospective phase 2 trials will provide further data on important issues, including optimal neoadjuvant treatment duration, ICI monotherapy vs combination, and the need for adjuvant ICI therapy.

Conclusions and Relevance

While this review found that early results of neoadjuvant immunotherapy for localized dMMR CRC show high rates of major and complete pathological response, longer-term follow-up data are needed to ensure that oncologic outcomes are not compromised and are ideally improved. Neoadjuvant ICI therapy in localized dMMR CRC represents a potential paradigm shift with implications for organ preservation.

Comprehensive Analysis of Disulfidptosis-Related LncRNAs in Molecular Classification, Immune Microenvironment Characterization and Prognosis of Gastric Cancer

BACKGROUND

Disulfidptosis is a novel form of programmed cell death that unveils promising avenues for the exploration of tumor treatment modalities. Gastric cancer (GC) is a malignant tumor characterized by high incidence and mortality rate. However, there has been no systematic study of disulfidptosis-related long noncoding RNAs (DRLs) signature in GC patients.

METHODS

The lncRNA expression profiles containing 412 GC samples were acquired from the Cancer Genome Atlas (TCGA) database. Differential expression analysis was performed alongside Pearson correlation analysis to identify DRLs. Prognostically significant DRLs were further screened using univariate COX regression analysis. Subsequently, LASSO regression and multifactorial COX regression analyses were employed to establish a risk signature composed of DRLs that exhibit independent prognostic significance. The predictive value of this risk signature was further validated in a test cohort. The ESTIMATE, CIBERSORT and ssGSEA methodologies were utilized to investigate the tumor immune microenvironment of GC populations with different DRLs profiles. Finally, the correlation between DRLs and various GC drug responses was explored.

RESULTS

We established a prognostic signature comprising 12 disulfidptosis-related lncRNAs (AC110491.1, AL355574.1, RHPN1-AS1, AOAH-IT1, AP001065.3, MEF2C-AS1, AC016394.2, LINC00705, LINC01952, PART1, TNFRSF10A-AS1, LINC01537). The Kaplan-Meier survival analysis revealed that patients in the high-risk group exhibited a poor prognosis. Both univariate and multivariate COX regression models demonstrated that the DRLs signature was an independent prognostic indicator in GC patients. Furthermore, the signature exhibited accurate predictions of survival at 1-, 3- and 5- years with the area under the curve (AUC) values of 0.708, 0.689 and 0.854, respectively. In addition, we also observed significant associations between the DRLs signature and various clinical variables, distinct immune landscape and drug sensitivity profiles in GC patients. The low-risk group patients may be more likely to benefit from immunotherapy and chemotherapy.

CONCLUSIONS

Our study investigated the role and potential clinical implications of DRLs in GC. The risk model constructed by DRLs demonstrated high accuracy in predicting the survival outcomes of GC and improving the treatment efficacy for GC patients.

Comprehensive genomic profiling for oncological advancements by precision medicine

Considerable advancements in next generation sequencing (NGS) techniques have sparked the use of comprehensive genomic profiling (CGP) as a guiding tool for precision-centered oncological treatments. The past two decades have seen the completion of the human genome project, and the consequential invention of NGS. High-throughput sequencing technologies support the discovery and commonplace use of individualized cancer treatments, specifically immune-centered checkpoint inhibitor therapies, and oncogene and tumor suppressor gene targeted therapies. Nevertheless, CGP is not commonly used in all clinical settings. This review investigates the clinically relevant applications of CGP. Studies published between the years 2000-2023 have shown substantial evidence of the benefits of integrating CGP into routine care practice, while also making important comparisons to current-standard oncological treatment strategies. Findings of a comprehensive genomic profile includes predictive, prognostic, and diagnostic biomarkers, together with somatic mutation identification which can indicate the efficacy of immunotherapies and molecularly guided therapies. This review highlights the importance of CGP in identifying driver mutations in tumors that subsequently can be effectively targeted with molecular therapeutics and lead to drug discovery, allowing for increased precision in treating tumors selectively based on their specific genetic mutations, thereby improving patient outcomes.

Integration of liquid biopsy and immunotherapy: opening a new era in colorectal cancer treatment

Immunotherapy has revolutionized the conventional treatment approaches for colorectal cancer (CRC), offering new therapeutic prospects for patients. Liquid biopsy has shown significant potential in early screening, diagnosis, and postoperative monitoring by analyzing circulating tumor cells (CTC) and circulating tumor DNA (ctDNA). In the era of immunotherapy, liquid biopsy provides additional possibilities for guiding immune-based treatments. Emerging technologies such as mass spectrometry-based detection of neoantigens and flow cytometry-based T cell sorting offer new tools for liquid biopsy, aiming to optimize immune therapy strategies. The integration of liquid biopsy with immunotherapy holds promise for improving treatment outcomes in colorectal cancer patients, enabling breakthroughs in early diagnosis and treatment, and providing patients with more personalized, precise, and effective treatment strategies.

Real-World Outcomes of Immunotherapy in Second- or Later-Line Non-Small Cell Lung Cancer with Actionable Genetic Alterations

INTRODUCTION

While the efficacy of immune checkpoint inhibitors (ICIs) in treating non-small cell lung cancer (NSCLC) patients with actionable genetic alterations (AGAs) is modest, certain patients demonstrate improved survival. Thus, this study aimed to evaluate the benefits of ICIs in NSCLC patients with diverse AGAs and verify the predictive biomarkers of ICI efficacy.

METHODS

From January 2018 to July 2022, this study compared the progression-free survival (PFS) of NSCLC patients with different AGAs treated with ICI monotherapy as second- or later-line therapy at Samsung Medical Center. To ascertain the predictors of ICIs efficacy, we adjusted ICIs' effects on PFS in terms of clinical and molecular biomarkers.

RESULTS

EGFR (46.0%) was the most prevalent mutation in 324 patients. In multivariate analysis, PD-L1 positivity (tumor proportion score (TPS) ≥ 1%) (HR = 0.41) and the use of steroids for immune-related adverse events (HR = 0.46) were positive factors for ICI therapy in the AGAs group. Co-existing mutation of STK11 with KRAS mutation (HR = 4.53) and TP53 with MET mutation (HR = 9.78) was negatively associated with survival.

CONCLUSIONS

The efficacy of ICI treatment varied across AGA subtypes, but patients with KRAS, MET, and BRAF mutations demonstrated relatively long-duration benefits of ICI therapy. PD-L1 was a significant positive predictive biomarker in all AGA groups.

Predicting response to immunotherapy in non-small cell lung cancer- from bench to bedside

Background

Immune checkpoint inhibitor (ICI) therapy is first-line treatment for many advanced non-small cell lung cancer (aNSCLC) patients. Predicting response could help guide selection of intensified or alternative anti-cancer regimens. We hypothesized that radiomics and laboratory variables predictive of ICI response in a murine model would also predict response in aNSCLC patients.

Methods

Fifteen mice with lung carcinoma tumors implanted in bilateral flanks received ICI. Pre-ICI laboratory and computed tomography (CT) data were evaluated for association with systemic ICI response. Baseline clinical and CT data for 117 aNSCLC patients treated with nivolumab were correlated with overall survival (OS). Models for predicting treatment response were created and subjected to internal cross-validation, with the human model further tested on 42 aNSCLC patients who received pembrolizumab.

Results

Models incorporating baseline NLR and identical radiomics (surface-to-mass ratio, average Gray, and 2D kurtosis) predicted ICI response in mice and OS in humans with AUCs of 0.91 and 0.75, respectively. The human model successfully sorted pembrolizumab patients by longer vs. shorter predicted OS (median 35 months vs. 6 months, p=0.026 by log-rank).

Discussion

This study advances precision oncology by non-invasively classifying aNSCLC patients according to ICI response using pre-treatment data only. Interestingly, identical radiomics features and NLR correlated with outcomes in the preclinical study and with ICI response in 2 independent patient cohorts, suggesting translatability of the findings. Future directions include using a radiogenomic approach to optimize modeling of ICI response.

Aligning germline and somatic mutations in prostate cancer. Are genetics changing practice?

OBJECTIVE

To review the current status of germline and somatic (tumour) genetic testing for prostate cancer (PCa), and its relevance for clinical practice.

METHODS

A narrative synthesis of various molecular profiles related to their clinical context was carried out. Current guidelines for genetic testing and its feasibility in clinical practice were analysed. We report the main identified genetic sequencing results or functional genomic scores for PCa published in the literature or obtained from the French PROGENE study.

RESULTS

The molecular alterations observed in PCa are mostly linked to disruption of the androgen receptor (AR) pathway or DNA repair deficiency. The main known germline mutations affect the BReast CAncer gene 2 (BRCA2) and homeobox B13 (HOXB13) genes, whereas AR and tumour protein p53 (TP53) are the genes with most frequent somatic alterations in tumours from men with metastatic PCa. Molecular tests are now available for detecting some of these germline or somatic alterations and sometimes recommended by guidelines, but their utilisation must combine rationality and feasibility. They can guide specific therapies, notably for the management of metastatic disease. Indeed, following androgen deprivation, targeted therapies for PCa currently include poly-(ADP-ribose)-polymerase (PARP) inhibitors, immune checkpoint inhibitors, and prostate-specific membrane antigen (PSMA)-guided radiotherapy. The genetic tests currently approved for targeted therapies remain limited to the detection of BRCA1 and BRCA2 mutation and DNA mismatch repair deficiency, while large panels are recommended for germline analyses, not only for inherited cancer predisposing syndrome, but also for metastatic PCa.

CONCLUSIONS

Further consensus aligning germline with somatic molecular analysis in metastatic PCa is required, including genomics scars, emergent immunohistochemistry, or functional pre-screen imaging. With rapid advances in knowledge and technology in the field, continuous updating of guidelines to help the clinical management of these individuals, and well-conducted studies to evaluate the benefits of genetic testing are needed.

Nanoassembly of doxorubicin-conjugated polyphosphoester and siRNA simultaneously elicited macrophage- and T cell- mediated anticancer immune response for cancer therapy

Efficiently reawakening immune cells, including T cells and macrophages, to eliminate tumor cells is a promising strategy for cancer treatment, but remains a huge challenge nowadays. Herein, a nanoassembly formed by doxorubicin (DOX)-conjugated polyphosphoester (PP-(hDOX)) and CD47-targeting siRNA (siCD47) via electrostatic and π-π stacking interactions, termed as PP-(hDOX&siCD47), was developed to reawaken the T cell and macrophage-mediated anticancer activity. The PP-(hDOX&siCD47) could efficiently blockade antiphagocytic signal by downregulation of CD47 expression to reactive macrophage-mediated anticancer immunotherapy. Moreover, the conjugated DOX of PP-(hDOX&siCD47) can perform the chemotherapy towards tumor cells and also elicit the T cell-mediated anticancer immune response via immunogenic cell death (ICD) effect. Therefore, the PP-(hDOX&siCD47) treatment could significantly increase M1-like macrophages proportion and tumor infiltration of CD8+ T cells, while the proportions of regulatory T cells (Treg) and myeloid-derived suppressor cells (MDSC) were considerably reduced in tumor tissue, eventually achieving significantly tumor growth inhibition. Overall, this study provides a simple siRNA and DOX codelivery approach to simultaneously elicit the macrophage- and T cell-mediated anticancer immune response for cancer therapy.

Novel strategies optimize immunotherapy by improving the cytotoxic function of T cells for pancreatic cancer treatment

Pancreatic cancer (PC) is considered highly malignant due to its unsatisfying prognosis and limited response to therapies. Immunotherapy has therefore been developed to harness the antigen-specific properties and cytotoxicity of the immune system, aiming to induce a robust anti-tumor immune response that specifically demolishes PC cells while minimizing lethality in healthy tissue. The activation and augmentation of cytotoxic T cells play a critical role in the initiation and final success of immunotherapy. PC, however, is often immunotherapy resistant due to its intrinsic immunosuppressive tumor microenvironment that consequently hampers effective T cell priming. Emerging therapeutic approaches are orientated to modulate the tumor microenvironment in PC to enhance immune system involvement and heighten T cell efficacy. These novel strategies have shown promising therapeutic effects in the treatment of PC either as standalone approaches or combinatorial with other therapeutic schemes. The objective of this article is to explore innovative approaches to optimize immunotherapy for PC patients through T cell cytotoxic function augmentation.

Next batter up! Targeting cancers with KRAS-G12D mutations

KRAS is the most frequently mutated oncogene in cancer. Activating mutations in codon 12, especially G12D, have the highest prevalence across a range of carcinomas and adenocarcinomas. With inhibitors to KRAS-G12D now entering clinical trials, understanding the biology of KRAS-G12D cancers, and identifying biomarkers that predict therapeutic response is crucial. In this Review, we discuss the genomics and biology of KRAS-G12D adenocarcinomas, including histological features, transcriptional landscape, the immune microenvironment, and how these factors influence response to therapy. Moreover, we explore potential therapeutic strategies using novel G12D inhibitors, leveraging knowledge gained from clinical trials using G12C inhibitors.

Emerging Prognostic and Predictive Factors in Pancreatic Cancer

Pancreatic cancer is a lethal disease with increasing incidence and high recurrence rates and is currently resistant to conventional therapies. Moreover, it displays extensive morphologic and molecular intratumoral and intertumoral heterogeneity and a mostly low mutational burden, failing to induce significant antitumor immunity. Thus, immunotherapy has shown limited effect in pancreatic cancer, except in rare tumors with microsatellite instability, constituting <1% of the cases. Currently, new methods, including single-cell and single-nucleus RNA sequencing, have refined and expanded the 2-group molecular classification based on bulk RNA sequencing (classical and basal-like subtypes), identifying hybrid forms and providing us with a comprehensive map of the tumor cell subsets that drive gene expression during tumor evolution, simultaneously giving us insight into therapy resistance and metastasis. Additionally, deeper profiling of the tumor microenvironment of pancreatic cancer by using spatial analyses and multiplex imaging techniques has improved our understanding of the heterogeneous distribution of both adaptive and innate immune components with their protumor and antitumor properties. By integrating host immune response patterns, as defined by spatial transcriptomic and proteomic analysis and multiplex immunofluorescence, with molecular and morphologic features of the tumors, we can increasingly understand the genetic, immunologic, and morphologic background of pancreatic cancer and recognize the potential predictors for different treatment modalities.

Organ Preservation in MSS Rectal Cancer

Rectal cancer is a heterogeneous disease with complex genetic and molecular subtypes. Emerging progress of neoadjuvant therapy has led to increased pathological and clinical complete response (cCR) rates for microsatellite stable (MSS) rectal cancer, which responds poorly to immune checkpoint inhibitor alone. As a result, organ preservation of MSS rectal cancer as an alternative to radical surgery has gradually become a feasible option. For patients with cCR or near-cCR after neoadjuvant treatment, organ preservation can be implemented safely with less morbidity. Patient selection can be done either before the neoadjuvant treatment for higher probability or after with careful assessment for a favorable outcome. Those patients who achieved a good clinical response are managed with nonoperative management, organ preservation surgery, or radiation therapy alone followed by strict surveillance. The oncological outcomes of patients with careful selection and organ preservation seem to be noninferior compared with those of radical surgery, with lower postoperative morbidity. However, more studies should be done to seek better regression of tumor and maximize the possibility of organ preservation in MSS rectal cancer.

The predictive value of tumor mutation burden on survival of gastric cancer patients treated with immune checkpoint inhibitors: A systematic review and meta-analysis

BACKGROUND

Tumor mutation burden (TMB) is a complement to traditional biomarkers related to the efficacy of immune checkpoint inhibitors (ICIs). The relationship between TMB and the efficacy of ICIs in gastric cancer was controversial. The systematic review and meta-analysis were conducted to investigate the predictive value of TMB on survival of gastric cancer patients treated with ICIs.

METHODS

We searched the databases PubMed, Embase, and Web of Science for articles, then screened eligible articles according to inclusion criteria. The effective data were extracted to calculate the pooled effects of hazard ratio (HR) for overall survival (OS) and progression-free survival (PFS), then perform publication bias, sensitivity analysis, and subgroup analysis by STATA 16.0.

RESULTS

The high TMB patients showed significantly longer survival than the low TMB patients (OS: HR 0.65,95% CI 0.55, 0.77, p < 0.001; PFS: HR 0.51, 95% CI 0.33, 0.77, p = 0.001). In the Asian subgroup, patients with high TMB exhibited better prognosis compared to low TMB (OS: HR 0.56, 95% CI 0.43, 0.72, p < 0.001; PFS: HR 0.45, 95% CI 0.28, 0.72, p = 0.001). In the non-Asian subgroup, the survival benefit was observed to be skewed toward patients with high TMB, but it was not statistically significant (OS:HR 0.61, 95% CI 0.32, 1.16, p = 0.133; PFS:HR 0.68, 95% CI 0.31, 1.48, p = 0.322).

CONCLUSIONS

This meta-analysis demonstrated that gastric cancer patients with high TMB showed significant benefits from ICIs compared to those with low TMB patients, particularly in Asian populations.

Gemcitabine and cisplatin plus nivolumab as organ-sparing treatment for muscle-invasive bladder cancer: a phase 2 trial

Cystectomy is a standard treatment for muscle-invasive bladder cancer (MIBC), but it is life-altering. We initiated a phase 2 study in which patients with MIBC received four cycles of gemcitabine, cisplatin, plus nivolumab followed by clinical restaging. Patients achieving a clinical complete response (cCR) could proceed without cystectomy. The co-primary objectives were to assess the cCR rate and the positive predictive value of cCR for a composite outcome: 2-year metastasis-free survival in patients forgoing immediate cystectomy or Collapse

Key Words

• phase ii trials
• translational research
• bladder cancer
• cancer immunotherapy
• chemotherapy

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MESH Headings

• Humans
• Antineoplastic Combined Chemotherapy Protocols/therapeutic use
• Cisplatin/therapeutic use
• Deoxycytidine/therapeutic use
• Disease-Free Survival
• Gemcitabine
• Muscles
• Neoadjuvant Therapy
• Neoplasm Invasiveness
• Nivolumab/therapeutic use
• Urinary Bladder Neoplasms/drug therapy
• Urinary Bladder Neoplasms/pathology
• Xeroderma Pigmentosum Group D Protein

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Grants

• U24 CA224319 NCI NIH HHS
• U01 DK124165 NIDDK NIH HHS
• U24 CA224285 NCI NIH HHS
• T32 CA078207 NCI NIH HHS
• S10 OD030463 NIH HHS
• P30 CA196521 NCI NIH HHS
• S10 OD026880 NIH HHS
• U24 CA224316 NCI NIH HHS
• U24 CA224331 NCI NIH HHS
• R01 CA249175 NCI NIH HHS
• V Foundation for Cancer Research (V Foundation)
• Bristol-Myers Squibb (Bristol-Myers Squibb Company)
• U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
• Foundation for the National Institutes of Health (FNIH)/Partnership for Accelerating Cancer Therapies (PACT)

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Affiliation(s)

Matthew D Galsky Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Siamak Daneshmand Department of Urology, Keck School of Medicine of USC, Norris Comprehensive Cancer Center, Los Angeles, CA, USA
Sudeh Izadmehr Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Edgar Gonzalez-Kozlova Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Kevin G Chan Department of Urology, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
Sara Lewis Department of Radiology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Bassam El Achkar Department of Radiology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Tanya B Dorff Department of Medical Oncology & Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
Jeremy Paul Cetnar Division of Hematology and Medical Oncology, Oregon Health and Science University, Portland, OR, USA
Brock O Neil Department of Urology, University of Utah, Salt Lake City, UT, USA
Anishka D'Souza Division of Hematology and Medical Oncology, Keck School of Medicine of USC, Norris Comprehensive Cancer Center, Los Angeles, CA, USA
Ronac Mamtani Division of Hematology and Medical Oncology, University of Pennsylvania Abramson Cancer Center, Philadelphia, PA, USA
Christos Kyriakopoulos Division of Hematology and Medical Oncology, University of Wisconsin Carbone Cancer Center, Madison, WI, USA
Tomi Jun Genentech, South San Francisco, CA, USA Formerly with the Icahn School of Medicine at Mount Sinai, New York, NY, USA
Mahalya Gogerly-Moragoda Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Rachel Brody Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Pathology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Hui Xie Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Kai Nie Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Geoffrey Kelly Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Amir Horowitz Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Yayoi Kinoshita Department of Pathology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Ethan Ellis Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Yohei Nose Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Giorgio Ioannou Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Rafael Cabal Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Diane M Del Valle Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
G Kenneth Haines Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Li Wang Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA Gene Dx, Stamford, CT, USA
Kent W Mouw Department of Radiation Oncology, Dana-Farber Cancer Institute/Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
Robert M Samstein Department of Radiation Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Reza Mehrazin Department of Urology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Nina Bhardwaj Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Menggang Yu Department of Biostatistics and Medical Informatics, University of Wisconsin Carbone Cancer Center, Madison, WI, USA
Qianqian Zhao Department of Biostatistics and Medical Informatics, University of Wisconsin Carbone Cancer Center, Madison, WI, USA
Seunghee Kim-Schulze Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Robert Sebra Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Jun Zhu Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA Gene Dx, Stamford, CT, USA
Sacha Gnjatic Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
John Sfakianos Department of Urology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Sumanta K Pal Department of Medical Oncology & Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, CA, USA

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