Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes

Altered extracellular matrix correlates with an immunosuppressive tumor microenvironment and disease progression in younger adults with oral cavity squamous cell carcinoma

Introduction

Oral cavity squamous cell carcinoma (OSCC) occurs most frequently in patients >60 years old with a history of tobacco and alcohol use. Epidemiological studies describe increased incidence of OSCC in younger adults (<45 years). Despite its poor prognosis, knowledge of OSCC tumor microenvironment (TME) characteristics in younger adults is scarce and could help inform possible resistance to emerging treatment options.

Methods

Patients with OSCC were evaluated using TCGA-HNSC (n=121) and a stage and subsite-matched institutional cohort (n=8) to identify differential gene expression focusing on the extracellular matrix (ECM) and epithelial-mesenchymal transition (EMT) processes in younger (≤45 years) vs. older adults (≥60 years). NanoString nCounter analysis was performed using isolated total RNA from formalin-fixed paraffin-embedded (FFPE) tumor samples. Stained tumor slides from young and old OSCC patients were evaluated for CD8+ T-cell counts using immunohistochemistry.

Results

Younger OSCC patients demonstrated significantly increased expression of ECM remodeling and EMT process genes, as well as TME immunosuppression. Gene set enrichment analyses demonstrated increased ECM pathways and concurrent decreased immune pathways in young relative to old patients. Transcripts per million of genetic markers involved in ECM remodeling including LAMB3, VCAN, S100A9, COL5A1, and ITGB2 were significantly increased in tumors of younger vs. older patients (adjusted p-value < 0.10). Young patient TMEs demonstrated a 2.5-fold reduction in CD8+ T-cells as compared to older patients (p < 0.05).

Conclusion

Differential gene expression impacting ECM remodeling and TME immunosuppression may contribute to disease progression in younger adult OSCC and has implications on response to evolving treatment modalities, such as immune checkpoint inhibitor therapy.

Duvelisib plus romidepsin in relapsed/refractory T cell lymphomas: a phase 1b/2a trial

PI3K-δ inhibitors have shown impressive activity in lymphoid malignancies but have been hampered by autoimmune and infectious toxicities, leading to market withdrawals. We previously demonstrated activity of the PI3K-δγ inhibitor duvelisib in T cell lymphomas (TCLs) that was associated with inflammatory adverse events. As reported here, we conducted a phase 1b/2a study of duvelisib in combination with either romidepsin (n = 66) or bortezomib (n = 32) in patients with relapsed/refractory TCL and found that the addition of romidepsin, but not bortezomib, appeared to increase efficacy while attenuating PI3K inhibitor-driven toxicity. The primary endpoint of the study was to determine the safety and maximum tolerated dose of duvelisib, which was 75 mg twice daily when combined with romidepsin versus 25 mg twice daily when combined with bortezomib. The most common adverse events were neutropenia (42%, 25/59) and fatigue (37%, 22/59) in patients treated with duvelisib and romidepsin and diarrhea (48%, 11/23) and neutropenia (30%, 7/23) in patients treated with duvelisib and bortezomib. Duvelisib and romidepsin resulted in less grade 3/4 hepatotoxicity (14%, 8/59) compared to 40% (14/35) in our previous study with duvelisib monotherapy. This was associated with reductions in circulating inflammatory mediators and myeloid cell inflammatory gene expression. Secondary endpoints of overall and complete response rates were 55% (35/64) and 34% (22/64) for patients treated with duvelisib and romidepsin and 34% (11/32) and 13% (4/32) for patients treated with duvelisib and bortezomib. Among patients with peripheral T cell lymphomas (PTCLs), overall and complete response rates of duvelisib and romidepsin were 56% (27/48) and 44% (21/48), respectively, with exploratory analyses showing increased response rates in patients with a follicular helper T cell subtype. These findings support further development of combined PI3K and histone deacetylase (HDAC) inhibition in TCLs and suggest a unique strategy to enable PI3K inhibitor-based combinations for additional patient populations. ClinicalTrials.gov identifier: NCT02783625 .

Tumor-associated macrophages restrict CD8+ T cell function through collagen deposition and metabolic reprogramming of the breast cancer microenvironment

Tumor progression is accompanied by fibrosis, a condition of excessive extracellular matrix accumulation, which is associated with diminished antitumor immune infiltration. Here we demonstrate that tumor-associated macrophages (TAMs) respond to the stiffened fibrotic tumor microenvironment (TME) by initiating a collagen biosynthesis program directed by transforming growth factor-β. A collateral effect of this programming is an untenable metabolic milieu for productive CD8+ T cell antitumor responses, as collagen-synthesizing macrophages consume environmental arginine, synthesize proline and secrete ornithine that compromises CD8+ T cell function in female breast cancer. Thus, a stiff and fibrotic TME may impede antitumor immunity not only by direct physical exclusion of CD8+ T cells but also through secondary effects of a mechano-metabolic programming of TAMs, which creates an inhospitable metabolic milieu for CD8+ T cells to respond to anticancer immunotherapies.

Liquid biopsy for human cancer: cancer screening, monitoring, and treatment

Currently, tumor treatment modalities such as immunotherapy and targeted therapy have more stringent requirements for obtaining tumor growth information and require more accurate and easy-to-operate tumor information detection methods. Compared with traditional tissue biopsy, liquid biopsy is a novel, minimally invasive, real-time detection tool for detecting information directly or indirectly released by tumors in human body fluids, which is more suitable for the requirements of new tumor treatment modalities. Liquid biopsy has not been widely used in clinical practice, and there are fewer reviews of related clinical applications. This review summarizes the clinical applications of liquid biopsy components (e.g., circulating tumor cells, circulating tumor DNA, extracellular vesicles, etc.) in tumorigenesis and progression. This includes the development process and detection techniques of liquid biopsies, early screening of tumors, tumor growth detection, and guiding therapeutic strategies (liquid biopsy-based personalized medicine and prediction of treatment response). Finally, the current challenges and future directions for clinical applications of liquid biopsy are proposed. In sum, this review will inspire more researchers to use liquid biopsy technology to promote the realization of individualized therapy, improve the efficacy of tumor therapy, and provide better therapeutic options for tumor patients.

Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in patients with cancer

The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.

Early Immune Remodeling Steers Clinical Response to First-Line Chemoimmunotherapy in Advanced Gastric Cancer

Adding anti-programmed cell death protein 1 (anti-PD-1) to 5-fluorouracil (5-FU)/platinum improves survival in some advanced gastroesophageal adenocarcinomas (GEA). To understand the effects of chemotherapy and immunotherapy, we conducted a phase II first-line trial (n = 47) sequentially adding pembrolizumab to 5-FU/platinum in advanced GEA. Using serial biopsy of the primary tumor at baseline, after one cycle of 5-FU/platinum, and after the addition of pembrolizumab, we transcriptionally profiled 358,067 single cells to identify evolving multicellular tumor microenvironment (TME) networks. Chemotherapy induced early on-treatment multicellular hubs with tumor-reactive T-cell and M1-like macrophage interactions in slow progressors. Faster progression featured increased MUC5A and MSLN containing treatment resistance programs in tumor cells and M2-like macrophages with immunosuppressive stromal interactions. After pembrolizumab, we observed increased CD8 T-cell infiltration and development of an immunity hub involving tumor-reactive CXCL13 T-cell program and epithelial interferon-stimulated gene programs. Strategies to drive increases in antitumor immune hub formation could expand the portion of patients benefiting from anti-PD-1 approaches.

SIGNIFICANCE

The benefit of 5-FU/platinum with anti-PD-1 in first-line advanced gastric cancer is limited to patient subgroups. Using a trial with sequential anti-PD-1, we show coordinated induction of multicellular TME hubs informs the ability of anti-PD-1 to potentiate T cell-driven responses. Differential TME hub development highlights features that underlie clinical outcomes. This article is featured in Selected Articles from This Issue, p. 695.

Transcriptomic-Based Microenvironment Classification Reveals Precision Medicine Strategies for Pancreatic Ductal Adenocarcinoma

BACKGROUND & AIMS

The complex tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) has hindered the development of reliable predictive biomarkers for targeted therapy and immunomodulatory strategies. A comprehensive characterization of the TME is necessary to advance precision therapeutics in PDAC.

METHODS

A transcriptomic profiling platform for TME classification based on functional gene signatures was applied to 14 publicly available PDAC datasets (n = 1657) and validated in a clinically annotated independent cohort of patients with PDAC (n = 79). Four distinct subtypes were identified using unsupervised clustering and assessed to evaluate predictive and prognostic utility.

RESULTS

TME classification using transcriptomic profiling identified 4 biologically distinct subtypes based on their TME immune composition: immune enriched (IE); immune enriched, fibrotic (IE/F); fibrotic (F); and immune depleted (D). The IE and IE/F subtypes demonstrated a more favorable prognosis and potential for response to immunotherapy compared with the F and D subtypes. Most lung metastases and liver metastases were subtypes IE and D, respectively, indicating the role of clonal phenotype and immune milieu in developing personalized therapeutic strategies. In addition, distinct TMEs with potential therapeutic implications were identified in treatment-naive primary tumors compared with tumors that underwent neoadjuvant therapy.

CONCLUSIONS

This novel approach defines a distinct subgroup of PADC patients that may benefit from immunotherapeutic strategies based on their TME subtype and provides a framework to select patients for prospective clinical trials investigating precision immunotherapy in PDAC. Further, the predictive utility and real-world clinical applicability espoused by this transcriptomic-based TME classification approach will accelerate the advancement of precision medicine in PDAC.

Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data

With the increased use of gene expression profiling for personalized oncology, optimized RNA sequencing (RNA-seq) protocols and algorithms are necessary to provide comparable expression measurements between exome capture (EC)-based and poly-A RNA-seq. Here, we developed and optimized an EC-based protocol for processing formalin-fixed, paraffin-embedded samples and a machine-learning algorithm, Procrustes, to overcome batch effects across RNA-seq data obtained using different sample preparation protocols like EC-based or poly-A RNA-seq protocols. Applying Procrustes to samples processed using EC and poly-A RNA-seq protocols showed the expression of 61% of genes (N = 20,062) to correlate across both protocols (concordance correlation coefficient > 0.8, versus 26% before transformation by Procrustes), including 84% of cancer-specific and cancer microenvironment-related genes (versus 36% before applying Procrustes; N = 1,438). Benchmarking analyses also showed Procrustes to outperform other batch correction methods. Finally, we showed that Procrustes can project RNA-seq data for a single sample to a larger cohort of RNA-seq data. Future application of Procrustes will enable direct gene expression analysis for single tumor samples to support gene expression-based treatment decisions.

Exploration of signature based on T cell-related genes in stomach adenocarcinoma by analysis of single cell sequencing data

BACKGROUND

Gastric cancer (GC) is a leading reason for the death of cancer around the world. The immune microenvironment counts a great deal in immunotherapy of advanced tumors, in which T cells exert an indispensable function.

METHODS

Single-cell RNA sequencing data were utilized to characterize the expression profile of T cells, followed by T cell-related genes (TCRGs) to construct signature and measure differences in survival time, enrichment pathways, somatic mutation status, immune status, and immunotherapy between groups.

RESULTS

The complex tumor microenvironment was analyzed by scRNA-seq data of GC patients. We screened for these T cell signature expression genes and the TCRGs-based signature was successfully constructed and relied on the riskscore grouping. In gene set enrichment analysis, it was shown that pro-tumor and suppressive immune pathways were more abundant in the higher risk group. We also found different infiltration of immune cells in two groups, and that the higher risk samples had a poorer response to immunotherapy.

CONCLUSION

Our study established a prognostic model, in which different groups had different prognosis, immune status, and enriched features. These results have provided additional insights into prognostic evaluation and the development of highly potent immunotherapies in GC.

The follicular lymphoma tumor microenvironment at single-cell and spatial resolution

ABSTRACT

Follicular lymphoma (FL) is a generally incurable malignancy that originates from developmentally blocked germinal center B cells residing, primarily, within lymph nodes (LNs). During the long natural history of FL, malignant B cells often disseminate to multiple LNs and can affect virtually any organ. Nonmalignant LNs are highly organized structures distributed throughout the body, in which they perform functions critical for host defense. In FL, the malignant B cells "re-educate" the lymphoid environment by altering the phenotype, distribution, and abundance of other cells such as T cells, macrophages, and subsets of stromal cells. Consequently, dramatic anatomical changes occur and include alterations in the number, shape, and size of neoplastic follicles with an accompanying attenuation of the T-cell zone. Ongoing and dynamic interactions between FL B cells and the tumor microenvironment (TME) result in significant clinical heterogeneity observed both within and across patients. Over time, FL evolves into pathological variants associated with distinct outcomes, ranging from an indolent disease to more aggressive clinical courses with early death. Given the importance of both cell-intrinsic and -extrinsic factors in shaping disease progression and patient survival, comprehensive examination of FL tumors is critical. Here, we describe the cellular composition and architecture of normal and malignant human LNs and provide a broad overview of emerging technologies for deconstructing the FL TME at single-cell and spatial resolution. We additionally discuss the importance of capturing samples at landmark time points as well as longitudinally for clinical decision-making.

Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer

To uncover the intricate, chemotherapy-induced spatiotemporal remodeling of the tumor microenvironment, we conducted integrative spatial and molecular characterization of 97 high-grade serous ovarian cancer (HGSC) samples collected before and after chemotherapy. Using single-cell and spatial analyses, we identify increasingly versatile immune cell states, which form spatiotemporally dynamic microcommunities at the tumor-stroma interface. We demonstrate that chemotherapy triggers spatial redistribution and exhaustion of CD8+ T cells due to prolonged antigen presentation by macrophages, both within interconnected myeloid networks termed "Myelonets" and at the tumor stroma interface. Single-cell and spatial transcriptomics identifies prominent TIGIT-NECTIN2 ligand-receptor interactions induced by chemotherapy. Using a functional patient-derived immuno-oncology platform, we show that CD8+T-cell activity can be boosted by combining immune checkpoint blockade with chemotherapy. Our discovery of chemotherapy-induced myeloid-driven spatial T-cell exhaustion paves the way for novel immunotherapeutic strategies to unleash CD8+ T-cell-mediated anti-tumor immunity in HGSC.

Multi-omic profiling of follicular lymphoma reveals changes in tissue architecture and enhanced stromal remodeling in high-risk patients

Follicular lymphoma (FL) is a generally incurable malignancy that evolves from developmentally blocked germinal center (GC) B cells. To promote survival and immune escape, tumor B cells undergo significant genetic changes and extensively remodel the lymphoid microenvironment. Dynamic interactions between tumor B cells and the tumor microenvironment (TME) are hypothesized to contribute to the broad spectrum of clinical behaviors observed among FL patients. Despite the urgent need, existing clinical tools do not reliably predict disease behavior. Using a multi-modal strategy, we examined cell-intrinsic and -extrinsic factors governing progression and therapeutic outcomes in FL patients enrolled onto a prospective clinical trial. By leveraging the strengths of each platform, we identify several tumor-specific features and microenvironmental patterns enriched in individuals who experience early relapse, the most high-risk FL patients. These features include stromal desmoplasia and changes to the follicular growth pattern present 20 months before first progression and first relapse.

Pan-cancer proteogenomics characterization of tumor immunity

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Tumor- and circulating-free DNA methylation identifies clinically relevant small cell lung cancer subtypes

Small cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, but implementing subtyping in the clinic has remained challenging, particularly due to limited tissue availability. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that subtype-specific patterns of DNA methylation could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts totaling 179 SCLC patients and using machine learning approaches, we report a highly accurate DNA methylation-based classifier (SCLC-DMC) that can distinguish SCLC subtypes. We further adjust the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and might guide precision SCLC therapy.

Evolutionary dynamics of tipifarnib in HRAS mutated head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a highly prevalent malignancy worldwide, with a significant proportion of patients developing recurrent and/or metastatic (R/M) disease. Despite recent advances in therapy, the prognosis for patients with advanced HNSCC remains poor. Here, we present the case of a patient with recurrent metastatic HNSCC harboring an HRAS G12S mutation who achieved a durable response to treatment with tipifarnib, a selective inhibitor of farnesyltransferase. The patient was a 48-year-old woman who had previously received multiple lines of therapy with no significant clinical response. However, treatment with tipifarnib resulted in a durable partial response that lasted 8 months. Serial genomic and transcriptomic analyses demonstrated upregulation of YAP1 and AXL in metastatic lesions compared with the primary tumor, the evolution of the tumor microenvironment from an immune-enriched to a fibrotic subtype with increased angiogenesis, and activation of the PI3K/AKT/mTOR pathway in tipifarnib treatment. Lastly, in HRAS-mutated PDXs and in the syngeneic HRAS model, we demonstrated that tipifarnib efficacy is limited by activation of the AKT pathway, and dual treatment with tipifarnib and the PI3K inhibitor, BYL719, resulted in enhanced anti-tumor efficacy. Our case study highlights the potential of targeting HRAS mutations with tipifarnib in R/M HNSCC and identifies potential mechanisms of acquired resistance to tipifarnib, along with immuno-, chemo-, and radiation therapy. Preclinical results provide a firm foundation for further investigation of drug combinations of HRAS-and PI3K -targeting therapeutics in R/M HRAS-driven HNSCC.

Metastasis and recurrence patterns in the molecular subtypes of urothelial bladder cancer

Urothelial cancer of the urinary bladder frequently metastasizes to lymph-nodes, lungs, liver and bone. A taxonomy for molecular classification exists, but it is unknown if molecular subtypes show tropism for different organs. Here, we study 146 patients with de novo metastatic disease or recurrence after curative treatment. We classify primary tumors using two transcriptomic methods and immunostaining and identify enrichment and depletion of metastatic sites in molecular subtypes using permutation tests. We observed significant depletion of bone metastases in the Basal/squamous molecular subtype, whereas the Urothelial-like subtype entailed an enrichment for metastases to bone. The Genomically unstable subtype was depleted of lung metastases, but enriched for atypical sites, including six out of seven patients with brain metastases. Stroma-rich primary tumor samples were associated with local recurrence, but not with distant sites. Additionally, the proportion with brain or testis metastases differed between systemic chemotherapy regimens (GC vs MVAC) suggesting a sanctuary effect. In conclusion, molecular subtypes of urothelial bladder cancer are significantly associated with specific metastatic sites, suggesting that subtype-specific molecular determinants could exist at various steps in the metastatic cascade.

STK11/LKB1-Deficient Phenotype Rather Than Mutation Diminishes Immunotherapy Efficacy and Represents STING/Type I Interferon/CD8+ T-Cell Dysfunction in NSCLC

INTRODUCTION

Conflicting findings have been reported regarding the association between STK11/LKB1 mutations and immune checkpoint inhibitor (ICB) efficacy in NSCLC. It has been reported that tumors could exhibit impaired STK11/LKB1 function even without STK11 mutations. We hypothesized that STK11 phenotype rather than mutation may better stratify ICB outcomes.

METHODS

Selected functional STK11 events and LKB1 protein data were leveraged to establish a transcriptomics-based classifier of STK11 phenotype (STK11-deficient [-def] or -proficient [-prof]). We analyzed in-house and Genentech/Roche's data of three randomized trials of programmed cell death protein-1 or programmed death-ligand 1 (PD-L1) inhibition in NSCLC (ORIENT-11, n = 171; OAK, n = 699; POPLAR, n = 192) and The Cancer Genome Atlas-NSCLC cohort.

RESULTS

Tissue STK11 mutation did not affect ICB outcomes. However, the survival benefit of ICB versus chemotherapy were lost or reversed in STK11-def tumors (hazard ratios for death, 95% confidence interval: OAK [0.97, 0.69-1.35]; POPLAR [1.61, 0.88-2.97]; ORIENT-11 [1.07, 0.50-2.29]), while remaining in STK11-prof tumors (hazard ratios for death, 95% confidence interval: OAK [0.81, 0.66-0.99]; POPLAR [0.66, 0.46-0.95]; ORIENT-11 [0.59, 0.37-0.92]). In tumors differentially classified by phenotype and mutation status, STK11-wild-type/def tumors had significantly worse ICB outcomes than STK11-mutated (STK11-MUT)/prof tumors (p < 0.05). The deleterious impact of STK11 deficiency was independent of STK11/KRAS/KEAP1 status or PD-L1 expression. The STING/interferon-I signaling, which was previously shown to be suppressed in STK11-MUT models, was perturbed in patients with STK11-def tumors rather than those with STK11-MUT tumors. Surprisingly, whereas high CD8+ T-cell infiltration was significantly associated with prolonged survival with ICB in STK11-prof tumors (p < 0.05 for 3 trials), it predicted an opposite trend toward worse ICB outcomes in STK11-def tumors across three trials. This suggested an association between STK11 deficiency and CD8+ T-cell dysfunction, which might not be reversed by programmed cell death protein 1 or PD-L1 blockade.

CONCLUSIONS

STK11 phenotype rather than mutation status can accurately identify patients with ICB-refractory NSCLC and reflect immune suppression. It can help refine stratification algorithms for future clinical research and also provide a reliable resource aiding basic and translational studies in identifying therapeutic targets.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Crosstalk with lung fibroblasts shapes the growth and therapeutic response of mesothelioma cells

Mesothelioma is an aggressive cancer of the mesothelial layer associated with an extensive fibrotic response. The latter is in large part mediated by cancer-associated fibroblasts which mediate tumour progression and poor prognosis. However, understanding of the crosstalk between cancer cells and fibroblasts in this disease is mostly lacking. Here, using co-cultures of patient-derived mesothelioma cell lines and lung fibroblasts, we demonstrate that fibroblast activation is a self-propagated process producing a fibrotic extracellular matrix (ECM) and triggering drug resistance in mesothelioma cells. Following characterisation of mesothelioma cells/fibroblasts signalling crosstalk, we identify several FDA-approved targeted therapies as far more potent than standard-of-care Cisplatin/Pemetrexed in ECM-embedded co-culture spheroid models. In particular, the SRC family kinase inhibitor, Saracatinib, extends overall survival well beyond standard-of-care in a mesothelioma genetically-engineered mouse model. In short, we lay the foundation for the rational design of novel therapeutic strategies targeting mesothelioma/fibroblast communication for the treatment of mesothelioma patients.

Integrative bioinformatics analysis and experimental validation of key biomarkers for risk stratification in primary biliary cholangitis

BACKGROUND

Primary biliary cholangitis (PBC) is an autoimmune liver disease, whose etiology is yet to be fully elucidated. Currently, ursodeoxycholic acid (UDCA) is the only first-line drug. However, 40% of PBC patients respond poorly to it and carry a potential risk of disease progression. So, in this study, we aimed to explore new biomarkers for risk stratification in PBC patients to enhance treatment.

METHODS

We first downloaded the clinical characteristics and microarray datasets of PBC patients from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Hub genes were further validated in multiple public datasets and PBC mouse model. Furthermore, we also verified the expression of the hub genes and developed a predictive model in our clinical specimens.

RESULTS

A total of 166 DEGs were identified in the GSE79850 dataset, including 95 upregulated and 71 downregulated genes. Enrichment analysis indicated that DEGs were significantly enriched in inflammatory or immune-related process. Among these DEGs, 15 risk-related genes were recognized and further validated in the GSE119600 cohort. Then, TXNIP, CD44, ENTPD1, and PDGFRB were identified as candidate hub genes. Finally, we proceeded to the next screening with these four genes in our serum samples and developed a three-gene panel. The gene panel could effectively identify those patients at risk of disease progression, yielding an AUC of 0.777 (95% CI, 0.657-0.870).

CONCLUSIONS

In summary, combining bioinformatics analysis and experiment validation, we identified TXNIP, CD44, and ENTPD1 as promising biomarkers for risk stratification in PBC patients.

MSI-XGNN: an explainable GNN computational framework integrating transcription- and methylation-level biomarkers for microsatellite instability detection

Microsatellite instability (MSI) is a hypermutator phenotype caused by DNA mismatch repair deficiency. MSI has been reported in various human cancers, particularly colorectal, gastric and endometrial cancers. MSI is a promising biomarker for cancer prognosis and immune checkpoint blockade immunotherapy. Several computational methods have been developed for MSI detection using DNA- or RNA-based approaches based on next-generation sequencing. Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play critical roles in the development and progression of cancer. We here developed MSI-XGNN, a new computational framework for predicting MSI status using bulk RNA-sequencing and DNA methylation data. MSI-XGNN is an explainable deep learning model that combines a graph neural network (GNN) model to extract features from the gene-methylation probe network with a CatBoost model to classify MSI status. MSI-XGNN, which requires tumor-only samples, exhibited comparable performance with two well-known methods that require tumor-normal paired sequencing data, MSIsensor and MANTIS and better performance than several other tools. MSI-XGNN also showed good generalizability on independent validation datasets. MSI-XGNN identified six MSI markers consisting of four methylation probes (EPM2AIP1|MLH1:cg14598950, EPM2AIP1|MLH1:cg27331401, LNP1:cg05428436 and TSC22D2:cg15048832) and two genes (RPL22L1 and MSH4) constituting the optimal feature subset. All six markers were significantly associated with beneficial tumor microenvironment characteristics for immunotherapy, such as tumor mutation burden, neoantigens and immune checkpoint molecules such as programmed cell death-1 and cytotoxic T-lymphocyte antigen-4. Overall, our study provides a powerful and explainable deep learning model for predicting MSI status and identifying MSI markers that can potentially be used for clinical MSI evaluation.

Trackable Intratumor Microdosing and Spatial Profiling Provide Early Insights into Activity of Investigational Agents in the Intact Tumor Microenvironment

PURPOSE

Cancer drug development is currently limited by a paradigm of preclinical evaluation that does not adequately recapitulate the complexity of the intact human tumor microenvironment (TME). To overcome this, we combined trackable intratumor microdosing (CIVO) with spatial biology readouts to directly assess drug effects in patient tumors in situ.

EXPERIMENTAL DESIGN

In a first-of-its-kind phase 0 clinical trial, we explored the effects of an investigational stage SUMOylation-activating enzyme (SAE) inhibitor, subasumstat (TAK-981) in 12 patients with head and neck carcinoma (HNC). Patients scheduled for tumor resection received percutaneous intratumor injections of subasumstat and vehicle control 1 to 4 days before surgery, resulting in spatially localized and graded regions of drug exposure (∼1,000-2,000 μm in diameter). Drug-exposed (n = 214) and unexposed regions (n = 140) were compared by GeoMx Digital Spatial Profiler, with evaluation at single-cell resolution in a subset of these by CosMx Spatial Molecular Imager.

RESULTS

Localized regions of subasumstat exposure revealed SUMO pathway inhibition, elevation of type I IFN response, and inhibition of cell cycle across all tumor samples. Single-cell analysis by CosMx demonstrated cell-cycle inhibition specific to the tumor epithelium, and IFN pathway induction commensurate with a TME shift from immune-suppressive to immune-permissive.

CONCLUSIONS

Pairing CIVO with spatial profiling enabled detailed investigation of response to subasumstat across a diverse sampling of native and intact TME. We demonstrate that drug mechanism of action can be directly evaluated in a spatially precise manner in the most translationally relevant setting: an in situ human tumor.

Deciphering transcriptomic determinants of the divergent link between PD-L1 and immunotherapy efficacy

Programmed cell death ligand 1 (PD-L1) expression remains the most widely used biomarker for predicting response to immune checkpoint inhibitors (ICI), but its predictiveness varies considerably. Identification of factors accounting for the varying PD-L1 performance is urgently needed. Here, using data from three independent trials comprising 1239 patients, we have identified subsets of cancer with distinct PD-L1 predictiveness based on tumor transcriptome. In the Predictiveness-High (PH) group, PD-L1+ tumors show better overall survival, progression-free survival, and objective response rate with ICI than PD-L1- tumors across three trials. However, the Predictiveness-Low (PL) group demonstrates an opposite trend towards better outcomes for PD-L1- tumors. PD-L1+ tumors from the PH group demonstrate the superiority of ICI over chemotherapy, whereas PD-L1+ tumors from the PL group show comparable efficacy between two treatments or exhibit an opposite trend favoring chemotherapy. This observation of context-dependent predictiveness remains strong regardless of immune subtype (Immune-Enriched or Non-Immune), PD-L1 regulation mechanism (adaptative or constitutive), tumor mutation burden, or neoantigen load. This work illuminates avenues for optimizing the use of PD-L1 expression in clinical decision-making and trial design, although this exploratory concept should be further confirmed in large trials.

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Deconvolution of cancer cell states by the XDec-SM method

Proper characterization of cancer cell states within the tumor microenvironment is a key to accurately identifying matching experimental models and the development of precision therapies. To reconstruct this information from bulk RNA-seq profiles, we developed the XDec Simplex Mapping (XDec-SM) reference-optional deconvolution method that maps tumors and the states of constituent cells onto a biologically interpretable low-dimensional space. The method identifies gene sets informative for deconvolution from relevant single-cell profiling data when such profiles are available. When applied to breast tumors in The Cancer Genome Atlas (TCGA), XDec-SM infers the identity of constituent cell types and their proportions. XDec-SM also infers cancer cells states within individual tumors that associate with DNA methylation patterns, driver somatic mutations, pathway activation and metabolic coupling between stromal and breast cancer cells. By projecting tumors, cancer cell lines, and PDX models onto the same map, we identify in vitro and in vivo models with matching cancer cell states. Map position is also predictive of therapy response, thus opening the prospects for precision therapy informed by experiments in model systems matched to tumors in vivo by cancer cell state.

Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma

Patients with mantle cell lymphoma (MCL), an incurable B-cell malignancy, benefit from accurate pretreatment disease stratification. We curated an extensive database of 862 patients diagnosed between 2014 and 2022. A machine learning (ML) gradient-boosted model incorporated baseline features from clinicopathologic, cytogenetic, and genomic data with high predictive power discriminating between patients with indolent or responsive MCL and those with aggressive disease (AUC ROC = 0.83). In addition, we utilized the gradient-boosted framework as a robust feature selection method for multivariate logistic and survival modeling. The best ML models incorporated features from clinical and genomic data types highlighting the need for correlative molecular studies in precision oncology. As proof of concept, we launched our most accurate and practical models using an application interface, which has potential for clinical implementation. We designated the 20-feature ML model-based index the "integrative MIPI" or iMIPI and a similar 10-feature ML index the "integrative simplified MIPI" or iMIPI-s. The top 10 baseline prognostic features represented in the iMIPI-s are: lactase dehydrogenase (LDH), Ki-67%, platelet count, bone marrow involvement percentage, hemoglobin levels, the total number of observed somatic mutations, TP53 mutational status, Eastern Cooperative Oncology Group performance level, beta-2 microglobulin, and morphology. Our findings emphasize that prognostic applications and indices should include molecular features, especially TP53 mutational status. This work demonstrates the clinical utility of complex ML models and provides further evidence for existing prognostic markers in MCL.

Significance

Our model is the first to integrate a dynamic algorithm with multiple clinical and molecular features, allowing for accurate predictions of MCL disease outcomes in a large patient cohort.

First-line durvalumab and tremelimumab with chemotherapy in RAS-mutated metastatic colorectal cancer: a phase 1b/2 trial

Although patients with microsatellite instable metastatic colorectal cancer (CRC) benefit from immune checkpoint blockade, chemotherapy with targeted therapies remains the only therapeutic option for microsatellite stable (MSS) tumors. The single-arm, phase 1b/2 MEDITREME trial evaluated the safety and efficacy of durvalumab plus tremelimumab combined with mFOLFOX6 chemotherapy in first line, in 57 patients with RAS-mutant unresectable metastatic CRC. Safety was the primary objective of phase Ib; no safety issue was observed. The phase 2 primary objective of efficacy in terms of 3-month progression-free survival (PFS) in patients with MSS tumors was met, with 3-month PFS of 90.7% (95% confidence interval (CI): 79.2-96%). For secondary objectives, response rate was 64.5%; median PFS was 8.2 months (95% CI: 5.9-8.6); and overall survival was not reached in patients with MSS tumors. We observed higher tumor mutational burden and lower genomic instability in responders. Integrated transcriptomic analysis underlined that high immune signature and low epithelial-mesenchymal transition were associated with better outcome. Immunomonitoring showed induction of neoantigen and NY-ESO1 and TERT blood tumor-specific T cell response associated with better PFS. The combination of durvalumab-tremelimumab with mFOLFOX6 was tolerable with promising clinical activity in MSS mCRC. Clinicaltrials.gov identifier: NCT03202758 .

B-Cell Infiltrate in the Tumor Microenvironment Is Associated With Improved Survival in Resected Lung Adenocarcinoma

Introduction

Relapse is common after resection of lung adenocarcinoma (LUAD). Features of the tumor microenvironment (TME) which influence postsurgical survival outcomes are poorly characterized. Here, we analyzed the TME of more than 1500 LUAD specimens to identify the relationship between B-cell infiltration and prognosis.

Methods

Whole exome sequencing and bulk RNA sequencing were performed on LUADs and adjacent normal lung tissue. Relapse-free survival and overall survival (OS) were retrospectively correlated with characteristics of the tumor and TME in three data sets.

Results

High B-cell content (defined as >10% B cells) was associated with improved OS in both a The Cancer Genome Atlas-resected LUAD data set (p = 0.01) and a separate institutional stage II LUAD data set (p = 0.04, median not reached versus 89.5 mo). A validation cohort consisting of pooled microarray data representing more than 1400 resected stage I to III LUADs confirmed the association between greater B-cell abundance, specifically higher B-cell expression, and longer postsurgical survival (median OS 90 versus 71 mo, p < 0.01). Relapse-free survival was longer for patients with adenocarcinomas with high B-cell content across data sets, but it did not reach statistical significance. Subcategorization of B-cell subsets indicated that high naive B-cell content was most predictive of survival. There was no correlation between programmed death-ligand 1 expression, lymphoid aggregates, or overall immune infiltrate density and survival outcomes across the cohorts.

Conclusions

The growing adjuvant immunotherapy repertoire has increased the urgency for identifying prognostic and predictive biomarkers. Comprehensive profiling of more than 1500 LUADs suggests that high tumor-infiltrating B-cell content is a favorable prognostic marker.

Next-generation deconvolution of transcriptomic data to investigate the tumor microenvironment

Methods for in silico deconvolution of bulk transcriptomics can characterize the cellular composition of the tumor microenvironment, quantifying the abundance of cell types associated with patients' prognosis and response to therapy. While first-generation deconvolution methods rely on precomputed, transcriptional signatures of a handful of cell types, second-generation methods can be trained with single-cell data to disentangle more fine-grained cell phenotypes and states. These novel approaches can also be applied to spatial transcriptomic data to reveal the spatial organization of tumors. In this review, we describe state-of-the-art deconvolution methods (first-generation, second-generation, and spatial) which can be used to investigate the tumor microenvironment, discussing their strengths and limitations. We conclude with an outlook on the challenges that need to be overcome to unlock the full potential of next-generation deconvolution for oncology and the life sciences.

Defining and using immune archetypes to classify and treat cancer

Tumours are surrounded by a host immune system that can suppress or promote tumour growth. The tumour microenvironment (TME) has often been framed as a singular entity, suggesting a single type of immune state that is defective and in need of therapeutic intervention. By contrast, the past few years have highlighted a plurality of immune states that can surround tumours. In this Perspective, we suggest that different TMEs have 'archetypal' qualities across all cancers - characteristic and repeating collections of cells and gene-expression profiles at the level of the bulk tumour. We discuss many studies that together support a view that tumours typically draw from a finite number (around 12) of 'dominant' immune archetypes. In considering the likely evolutionary origin and roles of these archetypes, their associated TMEs can be predicted to have specific vulnerabilities that can be leveraged as targets for cancer treatment with expected and addressable adverse effects for patients.

Unveiling the interplay between mutational signatures and tumor microenvironment: a pan-cancer analysis

Background

While recent studies have separately explored mutational signatures and the tumor microenvironment (TME), there is limited research on the associations of both factors in a pan-cancer context.

Materials and methods

We performed a pan-cancer analysis of over 8,000 tumor samples from The Cancer Genome Atlas (TCGA) project. Machine learning methods were employed to systematically explore the relationship between mutational signatures and TME and develop a risk score based on TME-associated mutational signatures to predict patient survival outcomes. We also constructed an interaction model to explore how mutational signatures and TME interact and influence cancer prognosis.

Results

Our analysis revealed a varied association between mutational signatures and TME, with the Clock-like signature showing the most widespread influence. Risk scores based on mutational signatures mainly induced by Clock-like and AID/APOBEC activity exhibited strong pan-cancer survival stratification ability. We also propose a novel approach to predict transcriptome decomposed infiltration levels using genome-derived mutational signatures as an alternative approach for exploring TME cell types when transcriptome data are unavailable. Our comprehensive analysis revealed that certain mutational signatures and their interaction with immune cells significantly impact clinical outcomes in particular cancer types. For instance, T cell infiltration levels only served as a prognostic biomarker in melanoma patients with high ultraviolet radiation exposure, breast cancer patients with high homologous recombination deficiency signature, and lung adenocarcinoma patients with high tobacco-associated mutational signature.

Conclusion

Our study comprehensively explains the complex interplay between mutational signatures and immune infiltration in cancer. The results highlight the importance of considering both mutational signatures and immune phenotypes in cancer research and their significant implications for developing personalized cancer treatments and more effective immunotherapy.

Mapping the functional interactions at the tumor-immune checkpoint interface

The interactions between tumor intrinsic processes and immune checkpoints can mediate immune evasion by cancer cells and responses to immunotherapy. It is, however, challenging to identify functional interactions due to the prohibitively complex molecular landscape of the tumor-immune interfaces. We address this challenge with a statistical analysis framework, immuno-oncology gene interaction maps (ImogiMap). ImogiMap quantifies and statistically validates tumor-immune checkpoint interactions based on their co-associations with immune-associated phenotypes. The outcome is a catalog of tumor-immune checkpoint interaction maps for diverse immune-associated phenotypes. Applications of ImogiMap recapitulate the interaction of SERPINB9 and immune checkpoints with interferon gamma (IFNγ) expression. Our analyses suggest that CD86-CD70 and CD274-CD70 immunoregulatory interactions are significantly associated with IFNγ expression in uterine corpus endometrial carcinoma and basal-like breast cancer, respectively. The open-source ImogiMap software and user-friendly web application will enable future applications of ImogiMap. Such applications may guide the discovery of previously unknown tumor-immune interactions and immunotherapy targets.

Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis via machine learning

Background: The precise diagnostic and prognostic biological markers were needed in immunotherapy for sepsis. Considering the role of necroptosis and immune cell infiltration in sepsis, differentially expressed necroptosis-related genes (DE-NRGs) were identified, and the relationship between DE-NRGs and the immune microenvironment in sepsis was analyzed.

Methods: Machine learning algorithms were applied for screening hub genes related to necroptosis in the training cohort. CIBERSORT algorithms were employed for immune infiltration landscape analysis. Then, the diagnostic value of these hub genes was verified by the receiver operating characteristic (ROC) curve and nomogram. In addition, consensus clustering was applied to divide the septic patients into different subgroups, and quantitative real-time PCR was used to detect the mRNA levels of the hub genes between septic patients (SP) (n = 30) and healthy controls (HC) (n = 15). Finally, a multivariate prediction model based on heart rate, temperature, white blood count and 4 hub genes was established.

Results: A total of 47 DE-NRGs were identified between SP and HC and 4 hub genes (BACH2, GATA3, LEF1, and BCL2) relevant to necroptosis were screened out via multiple machine learning algorithms. The high diagnostic value of these hub genes was validated by the ROC curve and Nomogram model. Besides, the immune scores, correlation analysis and immune cell infiltrations suggested an immunosuppressive microenvironment in sepsis. Septic patients were divided into 2 clusters based on the expressions of hub genes using consensus clustering, and the immune microenvironment landscapes and immune function between the 2 clusters were significantly different. The mRNA levels of the 4 hub genes significantly decreased in SP as compared with HC. The area under the curve (AUC) was better in the multivariate prediction model than in other indicators.

Conclusion: This study indicated that these necroptosis hub genes might have great potential in prognosis prediction and personalized immunotherapy for sepsis.

B cell-dependent subtypes and treatment-based immune correlates to survival in stage 3 and 4 lung adenocarcinomas

Lung cancer is the leading cause of cancer-related deaths worldwide. Surgery and chemoradiation are the standard of care in early stages of non-small cell lung cancer (NSCLC), while immunotherapy is the standard of care in late-stage NSCLC. The immune composition of the tumor microenvironment (TME) is recognized as an indicator for responsiveness to immunotherapy, although much remains unknown about its role in responsiveness to surgery or chemoradiation. In this pilot study, we characterized the NSCLC TME using mass cytometry (CyTOF) and bulk RNA sequencing (RNA-Seq) with deconvolution of RNA-Seq being performed by Kassandra, a recently published deconvolution tool. Stratification of patients based on the intratumoral abundance of B cells identified that the B-cell rich patient group had increased expression of CXCL13 and greater abundance of PD1+ CD8 T cells. The presence of B cells and PD1+ CD8 T cells correlated positively with the presence of intratumoral tertiary lymphoid structures (TLS). We then assessed the predictive and prognostic utility of these cell types and TLS within publicly available stage 3 and 4 lung adenocarcinoma (LUAD) RNA-Seq datasets. As previously described by others, pre-treatment expression of intratumoral 12-chemokine TLS gene signature is associated with progression free survival (PFS) in patients who receive treatment with immune checkpoint inhibitors (ICI). Notably and unexpectedly pre-treatment percentages of intratumoral B cells are associated with PFS in patients who receive surgery, chemotherapy, or radiation. Further studies to confirm these findings would allow for more effective patient selection for both ICI and non-ICI treatments.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

Preclinical models for prediction of immunotherapy outcomes and immune evasion mechanisms in genetically heterogeneous multiple myeloma

The historical lack of preclinical models reflecting the genetic heterogeneity of multiple myeloma (MM) hampers the advance of therapeutic discoveries. To circumvent this limitation, we screened mice engineered to carry eight MM lesions (NF-κB, KRAS, MYC, TP53, BCL2, cyclin D1, MMSET/NSD2 and c-MAF) combinatorially activated in B lymphocytes following T cell-driven immunization. Fifteen genetically diverse models developed bone marrow (BM) tumors fulfilling MM pathogenesis. Integrative analyses of ∼500 mice and ∼1,000 patients revealed a common MAPK-MYC genetic pathway that accelerated time to progression from precursor states across genetically heterogeneous MM. MYC-dependent time to progression conditioned immune evasion mechanisms that remodeled the BM microenvironment differently. Rapid MYC-driven progressors exhibited a high number of activated/exhausted CD8⁺ T cells with reduced immunosuppressive regulatory T (T_reg) cells, while late MYC acquisition in slow progressors was associated with lower CD8⁺ T cell infiltration and more abundant T_reg cells. Single-cell transcriptomics and functional assays defined a high ratio of CD8⁺ T cells versus T_reg cells as a predictor of response to immune checkpoint blockade (ICB). In clinical series, high CD8⁺ T/T_reg cell ratios underlie early progression in untreated smoldering MM, and correlated with early relapse in newly diagnosed patients with MM under Len/Dex therapy. In ICB-refractory MM models, increasing CD8⁺ T cell cytotoxicity or depleting T_reg cells reversed immunotherapy resistance and yielded prolonged MM control. Our experimental models enable the correlation of MM genetic and immunological traits with preclinical therapy responses, which may inform the next-generation immunotherapy trials.

Development and validation of an integrative pan-solid tumor predictor of PD-1/PD-L1 blockade benefit

BACKGROUND

Anti-PD-1 and PD-L1 (collectively PD-[L]1) therapies are approved for many advanced solid tumors. Biomarkers beyond PD-L1 immunohistochemistry, microsatellite instability, and tumor mutation burden (TMB) may improve benefit prediction.

METHODS

Using treatment data and genomic and transcriptomic tumor tissue profiling from an observational trial (NCT03061305), we developed Immunotherapy Response Score (IRS), a pan-tumor predictive model of PD-(L)1 benefit. IRS real-world progression free survival (rwPFS) and overall survival (OS) prediction was validated in an independent cohort of trial patients.

RESULTS

Here, by Cox modeling, we develop IRS-which combines TMB with CD274, PDCD1, ADAM12 and TOP2A quantitative expression-to predict pembrolizumab rwPFS (648 patients; 26 tumor types; IRS-High or -Low groups). In the 248 patient validation cohort (248 patients; 24 tumor types; non-pembrolizumab PD-[L]1 monotherapy treatment), median rwPFS and OS are significantly longer in IRS-High vs. IRS-Low patients (rwPFS adjusted hazard ratio [aHR] 0.52, p = 0.003; OS aHR 0.49, p = 0.005); TMB alone does not significantly predict PD-(L)1 rwPFS nor OS. In 146 patients treated with systemic therapy prior to pembrolizumab monotherapy, pembrolizumab rwPFS is only significantly longer than immediately preceding therapy rwPFS in IRS-High patients (interaction test p = 0.001). In propensity matched lung cancer patients treated with first-line pembrolizumab monotherapy or pembrolizumab+chemotherapy, monotherapy rwPFS is significantly shorter in IRS-Low patients, but is not significantly different in IRS-High patients. Across 24,463 molecularly-evaluable trial patients, 7.6% of patients outside of monotherapy PD-(L)1 approved tumor types are IRS-High/TMB-Low.

CONCLUSIONS

The validated, predictive, pan-tumor IRS model can expand PD-(L)1 monotherapy benefit outside currently approved indications.

Tumor-intrinsic SIRPA promotes sensitivity to checkpoint inhibition immunotherapy in melanoma

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD8+ T cells in a co-culture system. Mice bearing SIRPA-deficient melanoma tumors show no response to anti-PD-L1 treatment, whereas melanoma-specific SIRPA overexpression significantly enhances immunotherapy response. Mechanistically, SIRPA is regulated by its pseudogene, SIRPAP1. Our results suggest a complicated role of SIRPA in the tumor ecosystem, highlighting cell-type-dependent antagonistic effects of the same target on immunotherapy.

ImmCluster: an ensemble resource for immunology cell type clustering and annotations in normal and cancerous tissues

Single-cell transcriptome has enabled the transcriptional profiling of thousands of immune cells in complex tissues and cancers. However, subtle transcriptomic differences in immune cell subpopulations and the high dimensionality of transcriptomic data make the clustering and annotation of immune cells challenging. Herein, we introduce ImmCluster (http://bio-bigdata.hrbmu.edu.cn/ImmCluster) for immunology cell type clustering and annotation. We manually curated 346 well-known marker genes from 1163 studies. ImmCluster integrates over 420 000 immune cells from nine healthy tissues and over 648 000 cells from different tumour samples of 17 cancer types to generate stable marker-gene sets and develop context-specific immunology references. In addition, ImmCluster provides cell clustering using seven reference-based and four marker gene-based computational methods, and the ensemble method was developed to provide consistent cell clustering than individual methods. Five major analytic modules were provided for interactively exploring the annotations of immune cells, including clustering and annotating immune cell clusters, gene expression of markers, functional assignment in cancer hallmarks, cell states and immune pathways, cell-cell communications and the corresponding ligand-receptor interactions, as well as online tools. ImmCluster generates diverse plots and tables, enabling users to identify significant associations in immune cell clusters simultaneously. ImmCluster is a valuable resource for analysing cellular heterogeneity in cancer microenvironments.

Multiregional single-cell proteogenomic analysis of ccRCC reveals cytokine drivers of intratumor spatial heterogeneity

Intratumor heterogeneity (ITH) represents a major challenge for anticancer therapies. An integrated, multidimensional, multiregional approach dissecting ITH of the clear cell renal cell carcinoma (ccRCC) tumor microenvironment (TME) is employed at the single-cell level with mass cytometry (CyTOF), multiplex immunofluorescence (MxIF), and single-nucleus RNA sequencing (snRNA-seq) and at the bulk level with whole-exome sequencing (WES), RNA-seq, and methylation profiling. Multiregional analyses reveal unexpected conservation of immune composition within each individual patient, with profound differences among patients, presenting patient-specific tumor immune microenvironment signatures despite underlying genetic heterogeneity from clonal evolution. Spatial proteogenomic TME analysis using MxIF identifies 14 distinct cellular neighborhoods and, conversely, demonstrated architectural heterogeneity among different tumor regions. Tumor-expressed cytokines are identified as key determinants of the TME and correlate with clinical outcome. Overall, this work signifies that spatial ITH occurs in ccRCC, which may drive clinical heterogeneity and warrants further interrogation to improve patient outcomes.

Decoding tumor microenvironments through artificial tumor transcriptomes

In this issue of Cancer Cell, Zaitsev et al. (2022) present a machine-learning-based approach, trained from millions of artificial transcriptomes with admixed cell populations, for reconstructing tumor microenvironments (TMEs). The high accuracy of this approach, demonstrated through extensive validation, enables systematic investigation of TMEs in both research and clinical settings.