High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.

SMARCA4 deficiency and mutations are frequent in large cell lung carcinoma and are prognostically significant

SMARCA4 mutation has emerged as a marker of poor prognosis in lung cancer and has potential predictive value in cancer treatment, but recommendations for which patients require its investigation are lacking. We comprehensively studied SMARCA4 alterations and the clinicopathological significance in a large cohort of immunohistochemically-subtyped non-small cell lung cancer (NSCLC). A total of 1416 patients was studied for the presence of SMARCA4 deficiency by immunohistochemistry (IHC). Thereafter, comprehensive sequencing of tumours was performed for 397 of these patients to study the mutational spectrum of SWI/SNF and SMARCA4 aberrations. IHC evidence of SMARCA4 deficiency was found in 2.9% of NSCLC. Of the sequenced tumours, 38.3% showed aberration in SWI/SNF complex, and 9.3% had SMARCA4 mutations. Strikingly, SMARCA4 aberrations were much more prevalent in large cell carcinoma (LCC) than other histological tumour subtypes. SMARCA4-deficient and SMARCA4-mutated tumours accounted for 40.5% and 51.4% of all LCC, respectively. Multivariable analyses confirmed SMARCA4 mutation was an independent prognostic factor in lung cancer. The immunophenotype of a subset of these tumours frequently showed TTF1 negativity and HepPAR1 positivity. SMARCA4 mutation or its deficiency was associated with positive smoking history and poor prognosis. It also demonstrated mutual exclusion with EGFR mutation. Taken together, the high incidence of SMARCA4 aberrations in LCC may indicate its diagnostic and prognostic value. Our study established the necessity of SMARCA4 IHC in the identification of SMARCA4-aberrant tumours, and this may be of particular importance in LCC and tumours without known driver events.

Immunotherapy in Colorectal Cancer - Finding the Achilles' Heel

AbstractColorectal cancer treatment has evolved considerably in the last decade with the development of immunotherapies. Immune checkpoint inhibitor therapies have brisk and durable responses in patients with advanced microsatellite instability-high colorectal cancer, both surgically resectable and unresectable; however, patients with microsatellite stable colorectal cancer in general do not respond to the same therapy. Emerging evidence shows that immune checkpoint inhibitors may elicit responses in subsets of patients with microsatellite stable colorectal cancer, especially when combined with other anticancer agents that can modulate the tumor microenvironment. Therefore, rationally designed therapeutic combinations involving immune checkpoint inhibitors, as well as the development of predictive biomarkers for optimal patient selection, have emerged as two key areas of active research. In addition, other immunotherapeutic agents such as cell-based therapies and bispecific T-cell engagers are beginning to be studied in preclinical and early-phase settings. Although by no means a universal treatment strategy, immunotherapy can elicit responses in microsatellite stable colorectal cancer and further research is needed to extend their benefit to patients with microsatellite stable colorectal cancer. Here, we review the current state of immunotherapeutic regimens for microsatellite stable colorectal cancer.

Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology

In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI. This resemblance not only could facilitate their detection by a transformer-based Deep Learning (DL) system trained on MSI pathology slides, but also indicates the possibility for MSS patients with POLE mutations to access enhanced treatment options, which might otherwise be overlooked. To harness this potential, we trained a Deep Learning classifier on a large dataset with the ground truth for microsatellite status and subsequently validated its capabilities for MSI and POLE detection across three external cohorts. Our model accurately identified MSI status in both the internal and external resection cohorts using pathology images alone. Notably, with a classification threshold of 0.5, over 75% of POLE driver mutant patients in the external resection cohorts were flagged as "positive" by a DL system trained on MSI status. In a clinical setting, deploying this DL model as a preliminary screening tool could facilitate the efficient identification of clinically relevant MSI and POLE mutations in colorectal tumors, in one go.

Associations between immune cell phenotypes and lung cancer subtypes: insights from mendelian randomization analysis

INTRODUCTION

Lung cancer is a common malignant tumor, and different types of immune cells may have different effects on the occurrence and development of lung cancer subtypes, including lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). However, the causal relationship between immune phenotype and lung cancer is still unclear.

METHODS

This study utilized a comprehensive dataset containing 731 immune phenotypes from the European Bioinformatics Institute (EBI) to evaluate the potential causal relationship between immune phenotypes and LUSC and LUAD using the inverse variance weighted (IVW) method in Mendelian randomization (MR). Sensitivity analyses, including MR-Egger intercept, Cochran Q test, and others, were conducted for the robustness of the results. The study results were further validated through meta-analysis using data from the Transdisciplinary Research Into Cancer of the Lung (TRICL) data. Additionally, confounding factors were excluded to ensure the robustness of the findings.

RESULTS

Among the final selection of 729 immune cell phenotypes, three immune phenotypes exhibited statistically significant effects with LUSC. CD28 expression on resting CD4 regulatory T cells (OR 1.0980, 95% CI: 1.0627-1.1344, p < 0.0001) and CD45RA + CD28- CD8 + T cell %T cell (OR 1.0011, 95% CI: 1.0007; 1.0015, p < 0.0001) were associated with increased susceptibility to LUSC. Conversely, CCR2 expression on monocytes (OR 0.9399, 95% CI: 0.9177-0.9625, p < 0.0001) was correlated with a decreased risk of LUSC. However, no significant causal relationships were established between any immune cell phenotypes and LUAD.

CONCLUSION

This study demonstrates that specific immune cell types are associated with the risk of LUSC but not with LUAD. While these findings are derived solely from European populations, they still provide clues for a deeper understanding of the immunological mechanisms underlying lung cancer and may offer new directions for future therapeutic strategies and preventive measures.

Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer

BACKGROUND

Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for predicting the response to ICIs in gastric cancer, using H&E-stained whole slide images (WSI).

METHODS

This multicenter study retrospectively collected and analyzed H&E-stained WSIs and clinical data from 584 patients with gastric cancer. An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the therapeutic efficacy of immune checkpoint inhibition. Model performance was evaluated using metrics including the area under the curve (AUC), sensitivity, and specificity. Additionally, SHAP (SHapley Additive exPlanations) analysis was used to explain the model's predicted values as the sum of the attribution values for each input feature. Pathogenomics analysis was employed to explain the molecular mechanisms underlying the model's predictions.

RESULTS

Our pathomics-driven ensemble model effectively stratified the response to ICIs in training cohort (AUC 0.985 (95% CI 0.971 to 0.999)), which was further validated in internal validation cohort (AUC 0.921 (95% CI 0.839 to 0.999)), as well as in external validation cohort 1 (AUC 0.914 (95% CI 0.837 to 0.990)), and external validation cohort 2 (0.927 (95% CI 0.802 to 0.999)). The univariate Cox regression analysis revealed that the prediction signature of pathomics-driven ensemble model was a prognostic factor for progression-free survival in patients with gastric cancer who underwent immunotherapy (p<0.001, HR 0.35 (95% CI 0.24 to 0.50)), and remained an independent predictor after multivariable Cox regression adjusted for clinicopathological variables, (including sex, age, carcinoembryonic antigen, carbohydrate antigen 19-9, therapy regime, line of therapy, differentiation, location and programmed death ligand 1 (PD-L1) expression in all patients (p<0.001, HR 0.34 (95% CI 0.24 to 0.50)). Pathogenomics analysis suggested that the ensemble model is driven by molecular-level immune, cancer, metabolism-related pathways, and was correlated with the immune-related characteristics, including immune score, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data score, and tumor purity.

CONCLUSIONS

Our pathomics-driven ensemble model exhibited high accuracy and robustness in predicting the response to ICIs using WSIs. Therefore, it could serve as a novel and valuable tool to facilitate precision immunotherapy.

The Role of Microsatellite Instability/DNA Mismatch Repair Deficiency and Tumor Mutational Burden as Biomarkers in Predicting Response to Immunotherapy in Castration-resistant Prostate Cancer

Large trials of immune checkpoint inhibitors (ICIs) in castration-resistant prostate cancer (CRPC) have mostly failed. Biomarker-selected CRPC patients, especially those with high microsatellite instability (MSI-H), mismatch repair deficiency (dMMR), or elevated tumor mutational burden (TMB), may benefit from single-agent ICIs. Despite their rarity in CRPC (∼2-5%), identification of MSI-H, dMMR, or TMB-H could improve patient selection for immunotherapy.

Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models

Objective

To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated.

Methods

A total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the "rms" package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model.

Results

The mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model.

Conclusion

PD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Immune-tumor interaction dictates spatially directed evolution of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a poor-prognostic cancer type with extensive intra- and inter-patient heterogeneity in both genomic variations and tumor microenvironment (TME). However, the patterns and drivers of spatial genomic and microenvironmental heterogeneity of ESCC remain largely unknown. Here, we generated a spatial multi-omic atlas by whole-exome, transcriptome, and methylome sequencing of 507 tumor samples from 103 patients. We identified a novel tumor suppressor PREX2, accounting for 22% of ESCCs with frequent somatic mutations or hyper-methylation, which promoted migration and invasion of ESCC cells in vitro. Analysis of the TME and quantification of subclonal expansion indicated that ESCCs undergo spatially directed evolution, where subclones mostly originated from the tumor center but had a biased clonal expansion to the upper direction of the esophagus. Interestingly, we found upper regions of ESCCs often underwent stronger immunoediting with increased selective fitness, suggesting more stringent immune selection. In addition, distinct TMEs were associated with variable genomic and clinical outcomes. Among them, hot TME was associated with high immune evasion and subclonal heterogeneity. We also found that immunoediting, instead of CD8+ T cell abundance, acts as an independent prognostic factor of ESCCs. Importantly, we found significant heterogeneity in previously considered potential therapeutic targets, as well as BRCAness characteristics in a subset of patients, emphasizing the importance of focusing on heterogeneity in ESCC targeted therapy. Collectively, these findings provide novel insights into the mechanisms of the spatial evolution of ESCC and inform precision therapeutic strategies.

Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies

The field of oncology has been transformed by immune checkpoint inhibitors (ICI) and other immune-based agents; however, many patients do not receive a durable benefit. While biomarker assessments from pivotal ICI trials have uncovered certain mechanisms of resistance, results thus far have only scraped the surface. Mechanisms of resistance are as complex as the tumor microenvironment (TME) itself, and the development of effective therapeutic strategies will only be possible by building accurate models of the tumor-immune interface. With advancement of multi-omic technologies, high-resolution characterization of the TME is now possible. In addition to sequencing of bulk tumor, single-cell transcriptomic, proteomic, and epigenomic data as well as T-cell receptor profiling can now be simultaneously measured and compared between responders and nonresponders to ICI. Spatial sequencing and imaging platforms have further expanded the dimensionality of existing technologies. Rapid advancements in computation and data sharing strategies enable development of biologically interpretable machine learning models to integrate data from high-resolution, multi-omic platforms. These models catalyze the identification of resistance mechanisms and predictors of benefit in ICI-treated patients, providing scientific foundation for novel clinical trials. Moving forward, we propose a framework by which in silico screening, functional validation, and clinical trial biomarker assessment can be used for the advancement of combined immunotherapy strategies.

Role of the microbiota in response to and recovery from cancer therapy

Our understanding of how the microbiota affects the balance between response to and failure of cancer treatment by modulating the tumour microenvironment and systemic immune system has advanced rapidly in recent years. Microbiota-targeting interventions in patients with cancer are an area of intensive investigation. Promisingly, phase I-II clinical trials have shown that interventions such as faecal microbiota transplantation can overcome resistance to immune checkpoint blockade in patients with melanoma, improve therapeutic outcomes in treatment-naive patients and reduce therapy-induced immunotoxicities. Here, we synthesize the evidence showing that the microbiota is an important determinant of both cancer treatment efficacy and treatment-induced acute and long-term toxicity, and we discuss the complex and inter-related mechanisms involved. We also assess the potential of microbiota-targeting interventions, including bacterial engineering and phage therapy, to optimize the response to and recovery from cancer therapy.

The long and winding road to biomarkers for immunotherapy: a retrospective analysis of samples from patients with triple-negative breast cancer treated with pembrolizumab

BACKGROUND

Immune checkpoint blockade (ICB) in combination with chemotherapy improves outcome of patients with triple-negative breast cancer (TNBC) in metastatic and early settings. The identification of predictive biomarkers able to guide treatment decisions is challenging and currently limited to programmed death-ligand 1 (PD-L1) expression and high tumor mutational burden (TMB) in the advanced setting, with several limitations.

MATERIALS AND METHODS

We carried out a retrospective analysis of clinical-pathological and molecular characteristics of tumor samples from 11 patients with advanced TNBC treated with single-agent pembrolizumab participating in two early-phase clinical trials: KEYNOTE-012 and KEYNOTE-086. Clinical, imaging, pathological [i.e. tumor-infiltrating lymphocytes (TILs), PD-L1 status], RNA sequencing, and whole-exome sequencing data were analyzed. We compared our results with publicly available transcriptomic data from TNBC cohorts from TCGA and METABRIC.

RESULTS

Response to pembrolizumab was heterogeneous: two patients experienced exceptional long-lasting responses, six rapid progressions, and three relatively slower disease progression. Neither PD-L1 nor stromal TILs were significantly associated with response to treatment. Increased TMB values were observed in tumor samples from exceptional responders compared to the rest of the cohort (P = 3.4 × 10-4). Tumors from exceptional responders were enriched in adaptive and innate immune cell signatures. Expression of regulatory T-cell markers (FOXP3, CCR4, CCR8, TIGIT) was mainly observed in tumors from responders except for glycoprotein-A repetitions predominant (GARP), which was overexpressed in tumors from rapid progressors. GARP RNA expression in primary breast tumors from the public dataset was significantly associated with a worse prognosis.

CONCLUSIONS

The wide spectrum of clinical responses to ICB supports that TNBC is a heterogeneous disease. Tumors with high TMB respond better to ICB. However, the optimal cut-off of 10 mutations (mut)/megabase (Mb) may not reflect the complexity of all tumor subtypes, despite its approval as a tumor-agnostic biomarker. Further studies are required to better elucidate the relevance of the tumor microenvironment and its components as potential predictive biomarkers in the context of ICB.

Molecular biology and novel therapeutics for IDH mutant gliomas: The new era of IDH inhibitors

Gliomas with Isocitrate dehydrogenase (IDH) mutation represent a discrete category of primary brain tumors with distinct and unique characteristics, behaviors, and clinical disease outcomes. IDH mutations lead to aberrant high-level production of the oncometabolite D-2-hydroxyglutarate (D-2HG), which act as a competitive inhibitor of enzymes regulating epigenetics, signaling pathways, metabolism, and various other processes. This review summarizes the significance of IDH mutations, resulting upregulation of D-2HG and the associated molecular pathways in gliomagenesis. With the recent finding of clinically effective IDH inhibitors in these gliomas, this article offers a comprehensive overview of the new era of innovative therapeutic approaches based on mechanistic rationales, encompassing both completed and ongoing clinical trials targeting gliomas with IDH mutations.

Molecular and immunological characteristics of postoperative relapse in lymph node-positive esophageal squamous cell cancer

BACKGROUND

The molecular and immunological characteristics of primary tumors and positive lymph nodes in esophageal squamous cell carcinoma (ESCC) are unknown and the relationship with recurrence is unclear, which this study attempted to explore.

METHODS

A total of 30 ESCC patients with lymph node positive (IIB-IVA) were enrolled. Among them, primary tumor and lymph node specimens were collected from each patient, and subjected to 551-tumor-targeted DNA sequencing and 289-immuno-oncology RNA panel sequencing to identify the different molecular basis and immunological features, respectively.

RESULTS

The primary tumors exhibited a higher mutation burden than lymph nodes (p < 0.001). One-year recurrent ESCC exhibited a higher Mucin16 (MUC16) mutation rate (p = 0.038), as well as univariate and multivariate analysis revealed that MUC16 mutation is independent genetic factor associated with reduced relapse-free survival (univariate, HR: 5.39, 95% CI: 1.67-17.4, p = 0.005; multivariate, HR: 7.36, 95% CI: 1.79-30.23, p = 0.006). Transcriptomic results showed non-relapse group had higher cytolytic activity (CYT) score (p = 0.025), and was enriched in the IFN-α pathway (p = 0.036), while those in the relapsed group were enriched in the TNF-α/NF-κB (p = 0.001) and PI3K/Akt pathway (p = 0.014).

CONCLUSION

The difference in molecular characteristics between primary lesions and lymph nodes may be the cause of the inconsistent clinical outcomes. Mutations of MUC16 and poor immune infiltration are associated with rapid relapse of nodes-positive ESCC.

Stratifying ICIs-responsive tumor microenvironment in HCC: from parsing out immune-hypoxic crosstalk to clinically applicable MRI-radiomics models

BACKGROUND

We aimed to redefine Immune checkpoint inhibitors (ICIs)-responsive "hot" TME and develop a corresponding stratification model to maximize ICIs-efficacy in Hepatocellular Carcinoma (HCC).

METHODS

Hypoxic scores were designed, and the relevance to immunotherapy responses were validated in pan-cancers through single cell analysis. Multi-omics analysis using the hypoxic scores and immune infiltrate abundance was performed to redefine the ICIs-responsive TME subtype in HCC patients from TCGA (n = 363) and HCCDB database (n = 228). The immune hypoxic stress index (IHSI) was constructed to stratify the ICIs-responsive TME subtype, with exploring biological mechanism in vitro and in vivo. MRI-radiomics models were built for clinical applicability.

RESULTS

The hypoxic scores were lower in the dominant cell-subclusters of responders in pan-cancers. The higher immune infiltrate-normoxic (HIN) subtype was redefined as the ICIs-responsive TME. Stratification of the HIN subtype using IHSI effectively identified ICIs-responders in Melanoma (n = 122) and urological cancer (n = 22). TRAF3IP3, the constituent gene of IHSI, was implicated in ICIs-relevant "immune-hypoxic" crosstalk by stimulating MAVS/IFN-I pathway under normoxic condition. MRI-radiomics models assessing TRAF3IP3 with HIF1A expression (AUC > 0.80) screened ICIs-Responders in HCC cohort (n = 75).

CONCLUSION

The hypoxic-immune stratification redefined ICIs-responsive TME and provided MRI-Radiomics models for initial ICIs-responders screening, with IHSI facilitating further identification.

Agnostic drug development revisited

The advent of molecular profiling and the generalization of next generation sequencing in oncology has enabled the identification of patients who could benefit from targeted agents. Since the tumor-agnostic approval of pembrolizumab for patients with MSI-High tumors in 2017, different molecularly-guided therapeutics have been awarded approvals and progressively incorporated in the treatment landscape across multiple tumor types. As the number of tumor-agnostic targets considered druggable expands in the clinic, novel challenges will reshape the drug development field involving all the stakeholders in oncology. In this review, we provide an overview of current tumor-agnostic approvals and discuss promising candidate therapeutics for tumor-agnostic designation and challenges for their broad implementation.

To develop a prognostic model for neoadjuvant immunochemotherapy efficacy in esophageal squamous cell carcinoma by analyzing the immune microenvironment

Objective

The choice of neoadjuvant therapy for esophageal squamous cell carcinoma (ESCC) is controversial. This study aims to provide a basis for clinical treatment selection by establishing a predictive model for the efficacy of neoadjuvant immunochemotherapy (NICT).

Methods

A retrospective analysis of 30 patients was conducted, divided into Response and Non-response groups based on whether they achieved major pathological remission (MPR). Differences in genes and immune microenvironment between the two groups were analyzed through next-generation sequencing (NGS) and multiplex immunofluorescence (mIF). Variables most closely related to therapeutic efficacy were selected through LASSO regression and ROC curves to establish a predictive model. An additional 48 patients were prospectively collected as a validation set to verify the model's effectiveness.

Results

NGS suggested seven differential genes (ATM, ATR, BIVM-ERCC5, MAP3K1, PRG, RBM10, and TSHR) between the two groups (P < 0.05). mIF indicated significant differences in the quantity and location of CD3+, PD-L1+, CD3+PD-L1+, CD4+PD-1+, CD4+LAG-3+, CD8+LAG-3+, LAG-3+ between the two groups before treatment (P < 0.05). Dynamic mIF analysis also indicated that CD3+, CD8+, and CD20+ all increased after treatment in both groups, with a more significant increase in CD8+ and CD20+ in the Response group (P < 0.05), and a more significant decrease in PD-L1+ (P < 0.05). The three variables most closely related to therapeutic efficacy were selected through LASSO regression and ROC curves: Tumor area PD-L1+ (AUC= 0.881), CD3+PD-L1+ (AUC= 0.833), and CD3+ (AUC= 0.826), and a predictive model was established. The model showed high performance in both the training set (AUC= 0.938) and the validation set (AUC= 0.832). Compared to the traditional CPS scoring criteria, the model showed significant improvements in accuracy (83.3% vs 70.8%), sensitivity (0.625 vs 0.312), and specificity (0.937 vs 0.906).

Conclusion

NICT treatment may exert anti-tumor effects by enriching immune cells and activating exhausted T cells. Tumor area CD3+, PD-L1+, and CD3+PD-L1+ are closely related to therapeutic efficacy. The model containing these three variables can accurately predict treatment outcomes, providing a reliable basis for the selection of neoadjuvant treatment plans.

Molecular classification and intratumoral heterogeneity of gastric adenocarcinoma

Gastric cancers frequently harbor striking histological complexity and diversity between lesions as well as within single lesions, known as inter- and intratumoral heterogeneity, respectively. The latest World Health Organization Classification of Tumors designated more than 30 histological subtypes for gastric epithelial tumors, assigning 12 subtypes for gastric adenocarcinoma (GAD). Meanwhile, recent advances in genome-wide analyses have provided molecular aspects to the histological classification of GAD, and consequently revealed different molecular traits underlying these histological subtypes. Moreover, accumulating knowledge of comprehensive molecular profiles has led to establishing molecular classifications of GAD, which are often associated with clinical biomarkers for therapeutics and prognosis. However, most of our knowledge of GAD molecular profiles is based on inter-tumoral heterogeneity, and the molecular profiles underlying intratumoral heterogeneity are yet to be determined. In this review, recently established molecular classifications of GAD are introduced in the aspect of pathological diagnosis and are discussed in the context of intratumoral heterogeneity.

Hepatocellular Carcinoma: Current Drug Therapeutic Status, Advances and Challenges

Hepatocellular carcinoma (HCC) is the most common form of liver cancer, accounting for ~90% of liver neoplasms. It is the second leading cause of cancer-related deaths and the seventh most common cancer worldwide. Although there have been rapid developments in the treatment of HCC over the past decade, the incidence and mortality rates of HCC remain a challenge. With the widespread use of the hepatitis B vaccine and antiviral therapy, the etiology of HCC is shifting more toward metabolic-associated steatohepatitis (MASH). Early-stage HCC can be treated with potentially curative strategies such as surgical resection, liver transplantation, and radiofrequency ablation, improving long-term survival. However, most HCC patients, when diagnosed, are already in the intermediate or advanced stages. Molecular targeted therapy, followed by immune checkpoint inhibitor immunotherapy, has been a revolution in HCC systemic treatment. Systemic treatment of HCC especially for patients with compromised liver function is still a challenge due to a significant resistance to immune checkpoint blockade, tumor heterogeneity, lack of oncogenic addiction, and lack of effective predictive and therapeutic biomarkers.

The prognostic and immunological role of MCM3 in pan-cancer and validation of prognosis in a clinical lower-grade glioma cohort

Background: Previous studies have shown that MCM3 plays a key role in initiating DNA replication. However, the mechanism of MCM3 function in most cancers is still unknown. The aim of our study was to explore the expression, prognostic role, and immunological characteristics of MCM3 across cancers. Methods: We explored the expression pattern of MCM3 across cancers. We subsequently explored the prognostic value of MCM3 expression by using univariate Cox regression analysis. Spearman correlation analysis was performed to determine the correlations between MCM3 and immune-related characteristics, mismatching repair (MMR) signatures, RNA modulator genes, cancer stemness, programmed cell death (PCD) gene expression, tumour mutation burden (TMB), microsatellite instability (MSI), and neoantigen levels. The role of MCM3 in predicting the response to immune checkpoint blockade (ICB) therapy was further evaluated in four immunotherapy cohorts. Single-cell data from CancerSEA were analysed to assess the biological functions associated with MCM3 in 14 cancers. The clinical correlation and independent prognostic significance of MCM3 were further analysed in the TCGA and CGGA lower-grade glioma (LGG) cohorts, and a prognostic nomogram was constructed. Immunohistochemistry in a clinical cohort was utilized to validate the prognostic utility of MCM3 expression in LGG. Results: MCM3 expression was upregulated in most tumours and strongly associated with patient outcomes in many cancers. Correlation analyses demonstrated that MCM3 expression was closely linked to immune cell infiltration, immune checkpoints, MMR genes, RNA modulator genes, cancer stemness, PCD genes and the TMB in most tumours. There was an obvious difference in outcomes between patients with high MCM3 expression and those with low MCM3 expression in the 4 ICB treatment cohorts. Single-cell analysis indicated that MCM3 was mainly linked to the cell cycle, DNA damage and DNA repair. The expression of MCM3 was associated with the clinical features of LGG patients and was an independent prognostic indicator. Finally, the prognostic significance of MCM3 in LGG was validated in a clinical cohort. Conclusion: Our study suggested that MCM3 can be used as a potential prognostic marker for cancers and may be associated with tumour immunity. In addition, MCM3 is a promising predictor of immunotherapy responses.

Tumor immune dysfunction and exclusion subtypes in bladder cancer and pan-cancer: a novel molecular subtyping strategy and immunotherapeutic prediction model

BACKGROUND

Molecular subtyping is expected to enable precise treatment. However, reliable subtyping strategies for clinical application remains defective and controversial. Given the significance of tumor immune dysfunction and exclusion (TIDE), we aimed to develop a novel TIDE-based subtyping strategy to guide personalized immunotherapy in the bladder cancer (BC).

METHODS

Transcriptome data of BC was used to evaluate the heterogeneity and the status of TIDE patterns. Subsequently, consensus clustering was applied to classify BC patients based on TIDE marker-genes. Patients' clinicopathological, molecular features and signaling pathways of the different TIDE subtypes were well characterized. We also utilize the deconvolution algorithms to analyze the tumor microenvironment, and further explore the sensitivity and mechanisms of each subtype to immunotherapy. Furthermore, BC patient clinical information, real-world BC samples and urine samples were collected for the validation of our findings, which were used for RNA-seq analysis, H&E staining, immunohistochemistry and immunofluorescence staining, and enzyme-linked immunosorbent assay. Finally, we also explored the conservation of our novel TIDE subtypes in pan-cancers.

RESULTS

We identified 69 TIDE biomarker genes and classified BC samples into three subtypes using consensus clustering. Subtype I showed the lowest TIDE status and malignancy with the best prognosis and highest sensitivity to immune checkpoint blockade (ICB) treatment, which was enriched of metabolic related signaling pathways. Subtype III represented the highest TIDE status and malignancy with the poorest prognosis and resistance to ICB treatment, resulting from its inhibitory immune microenvironment and T cell terminal exhaustion. Subtype II was in a transitional state with intermediate TIDE level, malignancy, and prognosis. We further confirmed the existence and characteristics of our novel TIDE subtypes using real-world BC samples and collected patient clinical data. This subtyping method was proved to be more efficient than previous known methods in identifying non-responders to immunotherapy. We also propose that combining our TIDE subtypes with known biomarkers can potentially improve the sensitivity and specificity of these biomarkers. Moreover, besides guiding ICB treatment, this classification approach can assist in selecting the frontline or recommended drugs. Finally, we confirmed that the TIDE subtypes are conserved across the pan-tumors.

CONCLUSIONS

Our novel TIDE-based subtyping method can serve as a powerful clinical tool for BC and pan-cancer patients, and potentially guiding personalized therapy decisions for selecting potential beneficiaries and excluding resistant patients of ICB therapy.

RNA M6A modification shaping cutaneous melanoma tumor microenvironment and predicting immunotherapy response

Recent years have seen rising mortality rates linked to cutaneous melanoma (SKCM), despite advances in immunotherapy. Understanding RNA N6-methyladenosine (M6A) significance in SKCM is crucial for prognosis, tumor microenvironment (TME), immune cell presence, and immunotherapy efficacy. We analyzed 23 M6A regulators using SKCM samples from TCGA and GEO databases, identifying three M6A modification patterns linked to TME cell infiltration. Principal component analysis (PCA) yielded an M6A score for individual tumors, utilizing patient gene expression profiles and CNV data from TCGA. M6A modification patterns play a crucial role in SKCM development and progression, influencing tumor attributes such as inflammatory stage, subtype, TME interstitial activity, and genetic mutations. The M6A score independently predicts patient outcomes and correlates with improved response to immunotherapy, validated across anti-PD-1 and anti-PD-L1 therapy cohorts. M6A modifications significantly impact the TME landscape, with the M6A score serving as a predictive marker for immunotherapy response. Integrating M6A-related information into clinical practice could revolutionize SKCM management and treatment strategies.

FAT1 upregulation is correlated with an immunosuppressive tumor microenvironment and predicts unfavorable outcome of immune checkpoint therapy in non-small cell lung cancer

Background

Previous studies found that FAT1 was recurrently mutated and aberrantly expressed in multiple cancers, and the loss function of FAT1 promoted the formation of cancer-initiating cells in several cancers. However, in some types of cancer, FAT1 upregulation could lead to epithelial-mesenchymal transition (EMT). The role of FAT1 in cancer progression, which appears to be cancer-type-specific, is largely unknown.

Methods

QRT-PCR and immunochemistry were used to verify the expression of FAT1 in non-small cell lung cancer (NSCLC). QRT-PCR and Western blot were used to detect the influence of siFAT1 knockdown on the expression of potential targets of FAT1 in NSCLC cell lines. GEPIA, KM-plotter, CAMOIP, and ROC-Plotter were used to evaluate the association between FAT1 and clinical outcomes based on expression and clinical data from TCGA and immune checkpoint inhibitors (ICI) treated cohorts.

Results

We found that FAT1 upregulation was associated with the activation of TGF-β and EMT signaling pathways in NSCLC. Patients with a high FAT1 expression level tend to have a poor prognosis and hard to benefit from ICI therapy. Genes involved in TGF-β/EMT signaling pathways (SERPINE1, TGFB1/2, and POSTN) were downregulated upon knockdown of FAT1. Genomic and immunologic analysis showed that high cancer-associated fibroblast (CAF) abundance, decreased CD8+ T cells infiltration, and low TMB/TNB were correlated with the upregulation of FAT1, thus promoting an immunosuppressive tumor microenvironment (TME) which influence the effect of ICI-therapy.

Conclusion

Our findings revealed the pattern of FAT1 upregulation in the TME of patients with NSCLC, and demonstrated its utility as a biomarker for unfavorable clinical outcomes, thereby providing a potential therapeutic target for NSCLC treatment.

Development and validation of an interpretable radiomic signature for preoperative estimation of tumor mutational burden in lung adenocarcinoma

Background

Tumor mutational burden (TMB) is a promising biomarker for immunotherapy. The challenge of spatial and temporal heterogeneity and high costs weaken its power in clinical routine. The aim of this study is to estimate TMB preoperatively using a volumetric CT-based radiomic signature (rMB).

Methods

Seventy-one patients with resectable lung adenocarcinoma (LUAD) who underwent whole-exome sequencing (WXS) from 2011 to 2014 were enrolled from the institutional biobank of Tianjin Medical University Cancer Institute and Hospital (TMUCIH). Forty-nine LUAD patients with WXS from the Cancer Genome Atlas Program (TCGA) served as the external validation cohort. Computed tomography (CT) volumes were resampled to 1-mm isotropic, semi-automatically segmented, and manually adjusted by two radiologists. A total of 3,108 radiomic features were extracted via PyRadiomics and then harmonized across cohorts by ComBat. Features with inter-segmentation intra-class correlation coefficient (ICC) > 0.8, low collinearity, and significant univariate power were passed to the least absolute shrinkage and selection operator (LASSO)-logistic classifier to discriminate TMB-high/TMB-low at a threshold of 10 mut/Mb. The receiver operating characteristic (ROC) curve analysis and calibration curve were used to determine its efficiency. Shapley values (SHAP) attributed individual predictions to feature contributions. Clinical variables and circulating biomarkers were collected to find potential associations with TMB and rMB.

Results

The top frequently mutated genes significantly differed between the Chinese and TCGA cohorts, with a median TMB of 2.20 and 3.46 mut/Mb and 15 (21.12%) and 9 (18.37%) cases of TMB-high, respectively. After dimensionality reduction, rMB comprised 21 features, which reached an AUC of 0.895 (sensitivity = 0.867, specificity = 0.875, and accuracy = 0.873) in the discovery cohort and 0.878 (sensitivity = 1.0, specificity = 0.825, and accuracy = 0.857 in a consist cutoff) in the validation cohort. rMB of TMB-high patients was significantly higher than rMB of TMB-low patients in both cohorts (p < 0.01). rMB was well-calibrated in the discovery cohort and validation cohort (p = 0.27 and 0.74, respectively). The square-filtered gray-level concurrence matrix (GLCM) correlation was of significant importance in prediction. The proportion of circulating monocytes and the monocyte-to-lymphocyte ratio were associated with TMB, whereas the circulating neutrophils and lymphocyte percentage, original and derived neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio were associated with rMB.

Conclusion

rMB, an intra-tumor radiomic signature, could predict lung adenocarcinoma patients with higher TMB. Insights from the Shapley values may enhance persuasiveness of the purposed signature for further clinical application. rMB could become a promising tool to triage patients who might benefit from a next-generation sequencing test.

Agnostic Administration of Targeted Anticancer Drugs: Looking for a Balance between Hype and Caution

Many tumors have well-defined vulnerabilities, thus potentially allowing highly specific and effective treatment. There is a spectrum of actionable genetic alterations which are shared across various tumor types and, therefore, can be targeted by a given drug irrespective of tumor histology. Several agnostic drug-target matches have already been approved for clinical use, e.g., immune therapy for tumors with microsatellite instability (MSI) and/or high tumor mutation burden (TMB), NTRK1-3 and RET inhibitors for cancers carrying rearrangements in these kinases, and dabrafenib plus trametinib for BRAF V600E mutated malignancies. Multiple lines of evidence suggest that this histology-independent approach is also reasonable for tumors carrying ALK and ROS1 translocations, biallelic BRCA1/2 inactivation and/or homologous recombination deficiency (HRD), strong HER2 amplification/overexpression coupled with the absence of other MAPK pathway-activating mutations, etc. On the other hand, some well-known targets are not agnostic: for example, PD-L1 expression is predictive for the efficacy of PD-L1/PD1 inhibitors only in some but not all cancer types. Unfortunately, the individual probability of finding a druggable target in a given tumor is relatively low, even with the use of comprehensive next-generation sequencing (NGS) assays. Nevertheless, the rapidly growing utilization of NGS will significantly increase the number of patients with highly unusual or exceptionally rare tumor-target combinations. Clinical trials may provide only a framework for treatment attitudes, while the decisions for individual patients usually require case-by-case consideration of the probability of deriving benefit from agnostic versus standard therapy, drug availability, associated costs, and other circumstances. The existing format of data dissemination may not be optimal for agnostic cancer medicine, as conventional scientific journals are understandably biased towards the publication of positive findings and usually discourage the submission of case reports. Despite all the limitations and concerns, histology-independent drug-target matching is certainly feasible and, therefore, will be increasingly utilized in the future.

Comprehensive analyses of the cancer-associated fibroblast subtypes and their score system for prediction of outcomes and immunosuppressive microenvironment in prostate cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) drive cancer progression and treatment failure on one hand, while their tumor-restraining functions are also observed on the other. Recent single cell RNA sequencing (scRNA-seq) analyses demonstrates heterogeneity of CAFs and defines molecular subtypes of CAFs, which help explain their different functions. However, it remains unclear whether these CAF subtypes have the same or different biological/clinical implications in prostate cancer (PCa) or other malignancies.

METHODS

PCa cells were incubated with supernatant from normal fibroblasts and CAFs to assess their effects on cell behaviors. Sequencing, genomic, and clinical data were collected from TCGA, MSKCC, CPGEA and GEO databases. CAF molecular subtypes and total CAF scores were constructed and grouped into low and high groups based on CAF-specific gene expression. Progression free interval (PFI), clinicopathological features, telomere length, immune cell infiltration, drug treatment and somatic mutations were compared among CAF molecular subtypes and low/high score groups.

RESULTS

The PCa CAF-derived supernatant promoted PCa cell proliferation and invasion. Based on differentially expressed genes identified by scRNA-seq analyses, we classified CAFs into 6 molecular subtypes in PCa tumors, and each subtype was then categorized into score-high and low groups according to the subtype-specific gene expression level. Such score models in 6 CAF subtypes all predicted PFI. Telomeres were significantly shorter in high-score tumors. The total CAF score from 6 CAF subtypes was also associated with PFI in PCa patients inversely, which was consistent with results from cellular experiments. Immunosuppressive microenvironment occurred more frequently in tumors with a high CAF score, which was characterized by increased CTLA4 expression and indicated better responses to CTLA4 inhibitors. Moreover, this model can also serve as a useful PFI predictor in pan-cancers.

CONCLUSION

By combining scRNA-seq and bulk RNA-seq data analyses, we develop a CAF subtype score system as a prognostic factor for PCa and other cancer types. This model system also helps distinguish different immune-suppressive mechanisms in PCa, suggesting its implications in predicting response to immunotherapy. Thus, the present findings should contribute to personalized PCa intervention.

Case report: Temozolomide induced hypermutation indicates an unfavorable response to immunotherapy in patient with gliomas

Background

Temozolomide (TMZ) is a key component in the treatment of gliomas. Hypermutation induced by TMZ can be encountered in routine clinical practice, and its significance is progressively gaining recognition. However, the relationship between TMZ-induced hypermutation and the immunologic response remains controversial.

Case presentation

We present the case of a 38-year-old male patient who underwent five surgeries for glioma. Initially diagnosed with IDH-mutant astrocytoma (WHO grade 2) during the first two surgeries, the disease progressed to grade 4 in subsequent interventions. Prior to the fourth surgery, the patient received 3 cycles of standard TMZ chemotherapy and 9 cycles of dose-dense TMZ regimens. Genomic and immunologic analyses of the tumor tissue obtained during the fourth surgery revealed a relatively favorable immune microenvironment, as indicated by an immunophenoscore of 5, suggesting potential benefits from immunotherapy. Consequently, the patient underwent low-dose irradiation combined with immunoadjuvant treatment. After completing 4 cycles of immunotherapy, the tumor significantly shrank, resulting in a partial response. However, after a 6-month duration of response, the patient experienced disease progression. Subsequent analysis of the tumor tissue obtained during the fifth surgery revealed the occurrence of hypermutation, with mutation signature analysis attributing TMZ treatment as the primary cause. Unfortunately, the patient succumbed shortly thereafter, with a survival period of 126 months.

Conclusion

Patients subjected to a prolonged regimen of TMZ treatment may exhibit heightened vulnerability to hypermutation. This hypermutation induced by TMZ holds the potential to function as an indicator associated with unfavorable response to immunotherapy in gliomas.

Comprehensive Analysis of N6-Methylandenosine-Related lncRNAs in Clear Cell Renal Cell Carcinoma: A Correlation With Prognosis, Tumor Progression, and Therapeutic Response

This study aims to develop a prognostic signature based on m6A-related lncRNAs for clear cell renal cell carcinoma (ccRCC). Differential expression analysis and Pearson correlation analysis were used to identify m6A-related lncRNAs associated with patient outcomes in The Cancer Genome Atlas (TCGA) database. Our approach led to the development of an m6A-related lncRNA risk score (MRLrisk), formulated using six identified lncRNAs: NFE4, AL008729.2, AL139123.1, LINC02154, AC124854.1 and ARHGAP31-AS1. Higher MRLrisk was identified as a risk factor for patients' prognosis in ccRCC. Furthermore, an MRLrisk-based nomogram was developed and demonstrated as a reliable tool for prognosis prediction in ccRCC. Enrichment analysis and tumor mutation signature studies were conducted to investigate MRLrisk-related biological phenotypes. The tumor immune dysfunction and exclusion (TIDE) score was employed to infer patients' response to immunotherapy, indicating a negative correlation between high MRLrisk and immunotherapy response. Our focus then shifted to LINC02154 for deeper exploration. We assessed LINC02154 expression in 28 ccRCC/normal tissue pairs and 3 ccRCC cell lines through quantitative real-time polymerase chain reaction (qRT-PCR). Functional experiments, including EdU incorporation, flow cytometry and transwell assays, were performed to assess the role of LINC02154 in ccRCC cell functions, discovering that its downregulation hinders cancer cell proliferation and migration. Furthermore, the influence of LINC02154 on ccRCC cells' sensitivity to Sunitinib was explored using CCK-8 assays, demonstrating that decreased LINC02154 expression increases Sunitinib sensitivity. In summary, this study successfully developed an MRLrisk model with significant prognostic value for ccRCC and established LINC02154 as a critical biomarker and prospective therapeutic target in ccRCC management.

Distinct leukocyte populations and cytokine secretion profiles define tumoral and peritumoral areas in renal cell carcinoma

Renal cell carcinoma (RCC) is a common malignancy frequently diagnosed at the metastatic stage. We performed a comprehensive analysis of the tumor immune microenvironment (TIME) in RCC patients, including the peritumoral tissue microenvironment, to characterize the phenotypic patterns and functional characteristics of infiltrating immune cells. T cells from various compartments (peripheral blood, tumor, peritumoral area, and adjacent healthy renal tissue) were assessed using flow cytometry and Luminex analyses, both before and after T cell-specific stimulation, to evaluate activation status and migratory potential. Our findings demonstrated that tumor-infiltrating lymphocytes (TILs) exhibited heightened cytokine production compared to peritumoral T cells (pTILs), acting as the primary source of cytotoxic markers (IFN-γ, granzyme B, and FasL). CD8+ T cells primarily employed Fas Ligand for cytotoxicity, while CD4+ T cells relied on CD107a. In addition, a statistically significant negative correlation between patient mortality and the presence of CD4+CD107+ pTILs was demonstrated. The engagement with the PD-1/PD-L1 pathway was also more evident in CD4+ and CD8+ pTILs as opposed to TILs. PD-L1 expression in the non-leukocyte fraction of the tumor tissue was relatively lower than in their leukocytic counterparts and upon stimulation, peripheral blood T cells displayed much stronger responses to stimulation than TILs and pTILs. Our results suggest that tumor and peritumoral T cells exhibit limited responsiveness to additional activation signals, while peripheral T cells retain their capacity to respond to stimulatory signals.

Tumor Mutation Burden as a Cornerstone in Precision Oncology Landscapes: Effect of Panel Size and Uncertainty in Cutoffs

Tumor mutation burden (TMB) has profound implications for personalized cancer therapy, particularly immunotherapy. However, the size of the panel and the cutoff values for an accurate determination of TMB are still controversial. In this study, a pan-cancer analysis was performed on 22 cancer types from The Cancer Genome Atlas. The efficiency of gene panels of different sizes and the effect of cutoff values in accurate TMB determination was assessed on a large cohort using Whole Exome Sequencing data (n = 9929 patients) as the gold standard. Gene panels of four different sizes (i.e., 0.44-2.54 Mb) were selected for comparative analyses. The heterogeneity of TMB within and between cancer types is observed to be very high, and it becomes possible to obtain the exact TMB value as the size of the panel increases. In panels with limited size, it is particularly difficult to recognize patients with low TMB. In addition, the use of a general TMB cutoff can be quite misleading. The optimal cutoff value varies between 5 and 20, depending on the TMB distribution of the different tumor types. The use of comprehensive gene panels and the optimization of TMB cutoff values for different cancer types can make TMB a robust biomarker in precision oncology. Moreover, optimization of TMB can help accelerate translational medicine research, and by extension, delivery of personalized cancer care in the future.

Off-label prescribing of immune checkpoint inhibitor therapy at a single pediatric cancer center

BACKGROUND

Immune checkpoint inhibitors (ICI) have improved outcomes in a variety of adult cancers and are prescribed with increasing frequency across oncology. However, patterns of off-label use of ICI in pediatrics remain unclear.

METHODS

This is a single-institution, retrospective cohort study evaluating off-label ICI use in pediatric and young adult patients with cancer treated at our institution from 2014 to 2022. Response was based on clinician assessment derived from clinical records. Immune-related adverse events (iRAEs) were classified according to CTCAE v5.0.

RESULTS

We identified 50 unique patients treated with off-label ICI (28 with solid tumors, 20 with central nervous system (CNS) tumors, 2 with hematologic malignancies). At time of ICI initiation, only five patients (10%) had localized disease, and all but one patient was treated in the relapsed/refractory setting. All patients were treated with the FDA-approved weight-based dosing recommendations. Overall, there was disease control in 21 patients (42%), with best response including one complete response (melanoma), two partial responses (high-grade glioma, CNS nongerminomatous germ cell tumor), and 18 patients with stable disease. Forty-four patients (88%) eventually experienced disease progression. Among 22 patients (44%) experiencing iRAEs, 10 (20%) had a grade ≥3 irAE, 12 (24%) required corticosteroids, and 14 (28%) required ICI discontinuation. irAE occurrence was associated with significantly improved progression-free survival (HR 0.35; 95% CI: 0.18 to 0.68; p = 0.002) and overall survival (HR 0.33; 95% CI: 0.17 to 0.66; p = 0.002).

CONCLUSIONS

At our institution, ICI was most commonly prescribed in the relapsed/refractory setting to patients with metastatic disease. The treatment was generally well-tolerated in the pediatric population. The overall response rate was low, and the majority of patients eventually experienced disease progression. A few patients, however, had durable treatment responses. Further studies are needed to identify which pediatric patients are most likely to benefit from ICI.

Modulator of TMB-associated immune infiltration (MOTIF) predicts immunotherapy response and guides combination therapy

Patients with high tumor mutational burden (TMB) levels do not consistently respond to immune checkpoint inhibitors (ICIs), possibly because a high TMB level does not necessarily result in adequate infiltration of CD8+ T cells. Using bulk ribonucleic acid sequencing (RNA-seq) data from 9311 tumor samples across 30 cancer types, we developed a novel tool called the modulator of TMB-associated immune infiltration (MOTIF), which comprises genes that can determine the extent of CD8+ T cell infiltration prompted by a certain TMB level. We confirmed that MOTIF can accurately reflect the integrity and defects of the cancer-immunity cycle. By analyzing 84 human single-cell RNA-seq datasets from 32 types of solid tumors, we revealed that MOTIF can provide insights into the diverse roles of various cell types in the modulation of CD8+ T cell infiltration. Using pretreatment RNA-seq data from 13 ICI-treated cohorts, we validated the use of MOTIF in predicting CD8+ T cell infiltration and ICI efficacy. Among the components of MOTIF, we identified EMC3 as a negative regulator of CD8+ T cell infiltration, which was validated via in vivo studies. Additionally, MOTIF provided guidance for the potential combinations of programmed death 1 blockade with certain immunostimulatory drugs to facilitate CD8+ T cell infiltration and improve ICI efficacy.

Targeted DNA Sequencing of Cutaneous Melanoma Identifies Prognostic and Predictive Alterations

BACKGROUND

Cutaneous melanoma (CM) can be molecularly classified into four groups: BRAF mutant, NRAS mutant, NF1 mutant and triple wild-type (TWT) tumors lacking any of these three alterations. In the era of immune checkpoint inhibition (ICI) and targeted molecular therapy, the clinical significance of these groups remains unclear. Here, we integrate targeted DNA sequencing with comprehensive clinical follow-up in CM patients.

METHODS

This was a retrospective cohort study that assessed clinical and molecular features from patients with localized or metastatic CM who underwent targeted next-generation sequencing as part of routine clinical care. A total of 254 patients with CM who had a CLIA-certified targeted sequencing assay performed on their tumor tissue were included.

RESULTS

Of the 254 patients with cutaneous melanoma, 77 were BRAF mutant (30.3%), 77 were NRAS mutant (30.3%), 47 were NF1 mutant (18.5%), 33 were TWT (13.0%) and the remaining 20 (7.9%) carried mutations in multiple driver genes (BRAF/NRAS/NF1 co-mutated). The majority of this co-mutation group carried mutations in NF1 (n = 19 or 90%) with co-occurring mutations in BRAF or NRAS, often with a weaker oncogenic variant. Consistently, NF1 mutant tumors harbored numerous significantly co-altered genes compared to BRAF or NRAS mutant tumors. The majority of TWT tumors (n = 29, 87.9%) harbor a pathogenic mutation within a known Ras/MAPK signaling pathway component. Of the 154 cases with available TMB data, the median TMB was 20 (range 0.7-266 mutations/Mb). A total of 14 cases (9.1%) were classified as having a low TMB (≤5 mutations/Mb), 64 of 154 (41.6%) had an intermediate TMB (>5 and ≤20 mutations/Mb), 40 of 154 (26.0%) had a high TMB (>20 and ≤50 mutations/Mb) and 36 of 154 (23.4%) were classified as having a very high TMB (>50 mutations/Mb). NRAS mutant melanoma demonstrated significantly decreased overall survival on multivariable analysis (HR for death 2.95, 95% CI 1.13-7.69, p = 0.027, log-rank test) compared with other TCGA molecular subgroups. Of the 116 patients in our cohort with available treatment data, 36 received a combination of dual ICI with anti-CTLA4 and anti-PD1 inhibition as first-line therapy. Elevated TMB was associated with significantly longer progression-free survival following dual-agent ICI (HR 0.26, 95% CI 0.07-0.90, p = 0.033, log-rank test).

CONCLUSIONS

NRAS mutation in CMs correlated with significantly worse overall survival. Elevated TMB was associated with increased progression-free survival for patients treated with a combination of dual ICI, supporting the potential utility of TMB as a predictive biomarker for ICI response in melanoma.

Hybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy

We present a study where predictive mechanistic modeling is used in combination with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) therapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models (but may not be directly measurable in the clinic) and easily measurable quantities or characteristics (that are not always readily incorporated into predictive mechanistic models). The mechanistic model we have applied here can predict tumor response from CT or MRI imaging based on key mechanisms underlying checkpoint inhibitor therapy, and in the present work, its parameters were combined with readily-available clinical measures from 93 patients into a hybrid training set for a deep learning time-to-event predictive model. Analysis revealed that training an artificial neural network with both mechanistic modeling-derived and clinical measures achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when only mechanistic model-derived values or only clinical data were used. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in neural network decision making, and in increasing prediction accuracy, further supporting the advantage of our hybrid approach. We anticipate that many existing mechanistic models may be hybridized with deep learning methods in a similar manner to improve predictive accuracy through addition of additional data that may not be readily implemented in mechanistic descriptions.

Tumor mutational burden for the prediction of PD-(L)1 blockade efficacy in cancer: challenges and opportunities

Tumor mutational burden (TMB) is a biomarker that measures the number of somatic mutations in a tumor's genome. TMB has emerged as a predictor of response to immune checkpoint inhibitors (ICIs) in various cancer types, and several studies have shown that patients with high TMB have better outcomes when treated with programmed death-ligand 1-based therapies. Recently, the Food and Drug Administration has approved TMB as a companion diagnostic for the use of pembrolizumab in solid tumors. However, despite its potential, the use of TMB as a biomarker for immunotherapy efficacy is limited by several factors. Here we review the limitations of TMB in predicting immunotherapy outcomes in patients with cancer and discuss potential strategies to optimize its use in the clinic.

Pan-cancer and single-cell analyses identify CD44 as an immunotherapy response predictor and regulating macrophage polarization and tumor progression in colorectal cancer

Introduction

Cluster of differentiation (CD) 44 is a non-kinase cell surface transmembrane glycoprotein critical for tumor maintenance and progression.

Methods

We conducted a systematic analysis of the expression profile and genomic alteration profile of CD44 in 33 types of cancer. The immune characteristics of CD44 were comprehensively explored by TIMER2.0 and CIBERSORT. In addition, the CD44 transcriptional landscape was examined at the single-cell level. Then, Pseudotime trajectory analysis of CD44 gene expression was performed using Monocle 2, and CellChat was utilized to compare the crosstalk differences between CD44+monocytes and CD44- monocytes. Tumor immune dysfunction and exclusion (TIDE) was used to evaluate the predictive ability of CD44 for immune checkpoint blockade (ICB) responses. The effects of CD44 on colorectal cancer (CRC) and macrophage polarization were investigated by knocking down the expression of CD44 in HCT-116 cell and macrophages in vitro.

Results

The expression of CD44 elevated in most cancers, predicting unfavorable prognosis. In addditon, CD44 was correlation with immune cell infiltration and key immune regulators. CD44+ monocytes had a higher information flow intensity than CD44- monocytes. CD44 had good predictive ability for immune checkpoint blockade responses. Knockdown of CD44 inhibited the proliferation, migration, and invasion of HCT-116 cell in vitro. Knockdown of CD44 inhibited M2 macrophage polarization.

Discussion

These findings suggest that CD44 is involved in regulating tumor development, macrophage polarization, and has certain predictive value for patient clinical prognosis and response to immunotherapy.

Tissue-specific thresholds of mutation burden associated with anti-PD-1/L1 therapy benefit and prognosis in microsatellite-stable cancers

Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 or its ligand (PD-1/L1) have expanded the treatment landscape against cancers but are effective in only a subset of patients. Tumor mutation burden (TMB) is postulated to be a generic determinant of ICI-dependent tumor rejection. Here we describe the association between TMB and survival outcomes among microsatellite-stable cancers in a real-world clinicogenomic cohort consisting of 70,698 patients distributed across 27 histologies. TMB was associated with survival benefit or detriment depending on tissue and treatment context, with eight cancer types demonstrating a specific association between TMB and improved outcomes upon treatment with anti-PD-1/L1 therapies. Survival benefits were noted over a broad range of TMB cutoffs across cancer types, and a dose-dependent relationship between TMB and outcomes was observed in a subset of cancers. These results have implications for the use of cancer-agnostic and universal TMB cutoffs to guide the use of anti-PD-1/L1 therapies, and they underline the importance of tissue context in the development of ICI biomarkers.

A Deep Learning-Based Assessment Pipeline for Intraepithelial and Stromal Tumor-Infiltrating Lymphocytes in High-Grade Serous Ovarian Carcinoma

Tumor-infiltrating lymphocytes (TILs) are associated with improved survival in patients with epithelial ovarian cancer. However, the evaluation of TILs has not been applied to routine clinical practice because of reproducibility issues. We developed two convolutional neural network models to detect TILs and to determine their spatial location in whole slide images, and established a spatial assessment pipeline to objectively quantify intraepithelial and stromal TILs in patients with high-grade serous ovarian carcinoma. The predictions of the established models showed a significant positive correlation with the number of CD8+ T cells and immune gene expressions. We demonstrated that patients with a higher density of intraepithelial TILs had a significantly prolonged overall survival and progression-free survival in multiple cohorts. On the basis of the density of intraepithelial and stromal TILs, we classified patients into three immunophenotypes: immune inflamed, excluded, and desert. The immune-desert subgroup showed the worst prognosis. Gene expression analysis showed that the immune-desert subgroup had lower immune cytolytic activity and T-cell-inflamed gene-expression profile scores, whereas the immune-excluded subgroup had higher expression of interferon-γ and programmed death 1 receptor signaling pathway. Our established evaluation method provides detailed and comprehensive quantification of intraepithelial and stromal TILs throughout hematoxylin and eosin-stained slides, and has potential for clinical application for personalized treatment of patients with ovarian cancer.

DNAJB1-PRKACA fusion neoantigens elicit rare endogenous T cell responses that potentiate cell therapy for fibrolamellar carcinoma

Fibrolamellar carcinoma (FLC) is a liver tumor with a high mortality burden and few treatment options. A promising therapeutic vulnerability in FLC is its driver mutation, a conserved DNAJB1-PRKACA gene fusion that could be an ideal target neoantigen for immunotherapy. In this study, we aim to define endogenous CD8 T cell responses to this fusion in FLC patients and evaluate fusion-specific T cell receptors (TCRs) for use in cellular immunotherapies. We observe that fusion-specific CD8 T cells are rare and that FLC patient TCR repertoires lack large clusters of related TCR sequences characteristic of potent antigen-specific responses, potentially explaining why endogenous immune responses are insufficient to clear FLC tumors. Nevertheless, we define two functional fusion-specific TCRs, one of which has strong anti-tumor activity in vivo. Together, our results provide insights into the fragmented nature of neoantigen-specific repertoires in humans and indicate routes for clinical development of successful immunotherapies for FLC.

Identification of Molecular Subtype and Prognostic Signature for Prostate Adenocarcinoma based on Neutrophil Extracellular Traps

Background: Prostate adenocarcinoma (PRAD) is one of the most common cancers in male. Increasing evidences pointed out that Neutrophil Extracellular Traps (NETs) play an important role in tumor angiogenesis, tumor metastasis and drug resistance. However, limited systematic studies regarding the role of NETs in PRAD have been performed. Identification of biomarkers based on NETs might facilitate risk stratification which help optimizing the clinical strategies. Methods: NETs-related genes with differential expressions were identified between PRAD and adjacent normal tissues in TCGA-PRAD dataset. Consensus cluster analysis was performed to determine the PRAD subtypes based on the different-expressed NETs-related genes. The difference of pathway enrichment, infiltrating immune cell and genomic mutation were also evaluated between subtypes. LASSO cox regression analysis was conducted to construct a NETs-related prognostic signature. Result: We identified 19 NETs related genes with differential expressions between PRAD and adjacent normal tissue in TCGA-PRAD dataset. Two significant subtypes were identified based on these 19 genes by consensus cluster analysis, namely subtype 1 and subtype 2. Significant differences in prognosis, immune infiltration and tumor mutation burden were observed in subtypes. LASSO Cox regression analysis identified a NETs-associated prognostic signature including 13 genes, and this signature had a good performance in predicting the progression-free survival of PRAD patients. Further integrated analysis indicated that MMP9 mostly expressed in Mono/Macrophage cells might play a role in regulating NETs formation via neutrophil activation in PRAD. Conclusion: To sum up, the current study identified two NETs-related molecular subtypes and based on which constructed a prognostic signature for PRAD.

PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer

PURPOSE

Gene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity.

METHODS

PIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups.

RESULTS

PIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion.

CONCLUSION

The PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Intragenic Rearrangement Burden Associates with Immune Cell Infiltration and Response to Immune Checkpoint Blockade in Cancer

Immune checkpoint blockade (ICB) can induce durable cancer remission. However, only a small subset of patients gains benefits. While tumor mutation burden (TMB) differentiates responders from nonresponders in some cases, it is a weak predictor in tumor types with low mutation rates. Thus, there is an unmet need to discover a new class of genetic aberrations that predict ICB responses in these tumor types. Here, we report analyses of pan-cancer whole genomes which revealed that intragenic rearrangement (IGR) burden is significantly associated with immune infiltration in breast, ovarian, esophageal, and endometrial cancers, particularly with increased M1 macrophage and CD8+ T-cell signatures. Multivariate regression against spatially counted tumor-infiltrating lymphocytes in breast, endometrial, and ovarian cancers suggested that IGR burden is a more influential covariate than other genetic aberrations in these cancers. In the MEDI4736 trial evaluating durvalumab in esophageal adenocarcinoma, IGR burden correlated with patient benefits. In the IMVigor210 trial evaluating atezolizumab in urothelial carcinoma, IGR burden increased with platinum exposure and predicted patient benefit among TMB-low, platinum-exposed tumors. Altogether, we have demonstrated that IGR burden correlates with T-cell inflammation and predicts ICB benefit in TMB-low, IGR-dominant tumors, and in platinum-exposed tumors.

Enhancing anti-CD274 (PD-L1) targeting through combinatorial immunotherapy with bispecific antibodies and fusion proteins: from preclinical to phase II clinical trials

INTRODUCTION

Immune checkpoint inhibitors have achieved great success in the treatment of many different types of cancer. Programmed cell death protein ligand 1 (PD-L1, CD274) is a major immunosuppressive immune checkpoint and a target for several already approved monoclonal antibodies. Despite this, novel strategies are under development, as the overall response remains low.

AREAS COVERED

In this review, an overview of the current biomarkers for response to PD-L1 inhibitor treatment is given, followed by a discussion of potential novel biomarkers, including tumor mutational burden and circulating tumor DNA. Combinatorial immunotherapy is a potential novel strategy to increase the response to PD-L1 inhibitor treatment and currently, several interesting bispecific antibodies as well as bispecific fusion proteins are undergoing early clinical investigation. We focus on substances targeting PD-L1 and a secondary target, and a secondary immunomodulatory target like CTLA-4, TIGIT, or CD47.

EXPERT OPINION

Overall, the presented studies show anti-tumor activity of these combinatorial immunotherapeutic approaches. However, still relatively low response rates suggest a need for better biomarkers.

Effects of temozolomide on tumor mutation burden and microsatellite instability in melanoma cells

The efficacy of combination therapy with an immune checkpoint inhibitor (ICI) and cytotoxic chemotherapeutic agents has been investigated in cancer, including melanoma. Before ICIs were introduced, dacarbazine or temozolomide (TMZ) were used to treat melanoma. Several studies using glioma or colorectal cancer cells showed that TMZ can increase the tumor mutation burden (TMB) and induce mismatch repair (MMR) deficiency associated with microsatellite instability (MSI). These could increase immunoreactivity to an ICI, but this has not been evaluated in melanoma cells. We investigated the effects of TMZ on MSI status and TMB in melanoma cells. To evaluate the TMB, we performed whole-exome sequencing using genomic DNA from the human melanoma cell lines Mel18, A375, WM266-4, G361, and TXM18 before and after TMZ treatment. Polymerase chain reaction amplification of five mononucleotide repeat markers, BAT25, BAT26, NR21, NR24, and MONO27, was performed, and we analyzed changes in the MSI status. In all cell lines, the TMB was increased after TMZ treatment (the change amount of TMB with ≤ 5% variant allele frequency [VAF] was 18.0-38.3 mutations per megabase) even in the condition without obvious cytological damage. MSI after TMZ treatment was not observed in any cells. TMZ increased TMB but did not change MSI status in melanoma cells.

STING signalling compensates for low tumour mutation burden to drive anti-tumour immunity

BACKGROUND

While mutation-derived neoantigens are well recognized in generating anti-tumour T cell response, increasing evidences highlight the complex association between tumour mutation burden (TMB) and tumour infiltrating lymphocytes (TILs). The exploration of non-TMB determinants of active immune response could improve the prognosis prediction and provide guidance for current immunotherapy.

METHODS

The transcriptomic and whole exome sequence data in The Cancer Genome Atlas were used to examine the relationship between TMB and exhausted CD8+ T cells (Tex), as an indicator of tumour antigen-specific T cells across nine major cancer types. Computational clustering analysis was performed on 4510 tumours to identify different immune profiles. NanoString gene expression analysis and single cell RNA-seq analysis using fresh human breast cancer were performed for finding validation.

FINDINGS

TMB was found to be poorly correlated with active immune response in various cancer types. Patient clustering analysis revealed a group of tumours with abundant Tex but low TMB. In those tumours, we observed significantly higher expression of the stimulator of interferon genes (STING) signalling. Dendritic cells, particularly those of BATF3+ lineage, were also found to be essential for accumulation of Tex within tumours. Mechanistically, loss of genomic and cellular integrity, marked by decreased DNA damage repair, defective replication stress response, and increased apoptosis were shown to drive STING activation.

INTERPRETATION

These results highlight that TMB alone does not fully predict tumour immune profiles, with STING signalling compensating for low TMB in non-hypermutated tumours to enhance anti-tumour immunity. Translating these results, STING agonists may benefit patients with non-hypermutated tumours. STING activation may serve as an additional biomarker to predict response to immune checkpoint blockades alongside TMB. Our research also unravelled the interplay between genomic instability and STING activation, informing potential combined chemotherapy targeting the axis of genomic integrity and immunotherapy.

FUNDING

City of Hope Christopher Family Endowed Innovation Fund for Alzheimer's Disease and Breast Cancer Research in honor of Vineta Christopher; Breast Cancer Alliance Early Career Investigator Award; National Cancer Institute of the National Institutes of Health under award number R01CA256989 and R01CA240392.

Prognostic and predictive biomarkers in melanoma

Biomarkers help to inform the clinical management of patients with melanoma. For patients with clinically localised primary melanoma, biomarkers can help to predict post-surgical outcome (including via the use of risk prediction tools), better select patients for sentinel lymph node biopsy, and tailor catch-all follow-up protocols to the individual. Systemic drug treatments, including immune checkpoint inhibitor (ICI) therapies and BRAF-targeted therapies, have radically improved the prognosis of metastatic (stage III and IV) cutaneous melanoma patients, and also shown benefit in the earlier setting of stage IIB/C primary melanoma. Unfortunately, a response is far from guaranteed. Here, we review clinically relevant, established, and emerging, prognostic, and predictive pathological biomarkers that refine clinical decision-making in primary and metastatic melanoma patients. Gene expression profile assays and nomograms are emerging tools for prognostication and sentinel lymph node risk prediction in primary melanoma patients. Biomarkers incorporated into clinical practice guidelines include BRAF V600 mutations for the use of targeted therapies in metastatic cutaneous melanoma, and the HLA-A∗02:01 allele for the use of a bispecific fusion protein in metastatic uveal melanoma. Several predictive biomarkers have been proposed for ICI therapies but have not been incorporated into Australian clinical practice guidelines. Further research, validation, and assessment of clinical utility is required before more prognostic and predictive biomarkers are fluidly integrated into routine care.

Moving toward precision medicine to predict drug sensitivity in patients with metastatic breast cancer

Tumor heterogeneity represents a major challenge in breast cancer, being associated with disease progression and treatment resistance. Precision medicine has been extensively applied to dissect tumor heterogeneity and, through a deeper molecular understanding of the disease, to personalize therapeutic strategies. In the last years, technological advances have widely improved the understanding of breast cancer biology and several trials have been developed to translate these new insights into clinical practice, with the ultimate aim of improving patients' outcomes. In the era of molecular oncology, genomics analyses and other methodologies are shaping a new treatment algorithm in breast cancer care. In this manuscript, we review the main steps of precision medicine to predict drug sensitivity in breast cancer from a translational point of view. Genomic developments and their clinical implications are discussed, along with technological advancements that could broaden precision medicine applications. Current achievements are put into perspective to provide an overview of the state-of-art of breast cancer precision oncology as well as to identify future research directions.