Motivation
Signal transduction networks regulate many essential biological processes and are frequently aberrated in diseases such as cancer. A mechanistic understanding of such networks, and how they differ between cell populations, is essential to design effective treatment strategies. Typically, such networks are computationally reconstructed based on systematic perturbation experiments, followed by quantification of signaling protein activity. Recent technological advances now allow for the quantification of the activity of many (signaling) proteins simultaneously in single cells. This makes it feasible to reconstruct or quantify signaling networks without performing systematic perturbations.

Results
Here, we introduce single-cell modular response analysis (scMRA) and single-cell comparative network reconstruction (scCNR) to derive signal transduction networks by exploiting the heterogeneity of single-cell (phospho-)protein measurements. The methods treat stochastic variation in total protein abundances as natural perturbation experiments, whose effects propagate through the network and hence facilitate the reconstruction and quantification of the underlying signaling network. scCNR reconstructs cell population-specific networks, where cells from different populations have the same underlying topology, but the interaction strengths can differ between populations. We extensively validated scMRA and scCNR on simulated data, and applied it to unpublished data of (phospho-)protein measurements of EGFR-inhibitor-treated keratinocytes to recover signaling differences downstream of EGFR. scCNR will help to unravel the mechanistic signaling differences between cell populations, and will subsequently guide the development of well-informed treatment strategies.

Studies have identified a link between specific microbiome-derived bacteria and immune checkpoint blockade (ICB) efficacy. However, these species lack consistency across studies, and their immunomodulatory mechanisms remain elusive. To understand the influence of the microbiome on ICB response, we studied its functional capacity. Using pan-cancer metagenomics data from ICB-treated patients, we showed that community-level metabolic pathways are stable across individuals, making them suitable for predicting ICB response. We identified several microbial metabolic processes significantly associated with response, including the methylerythritol 4-phosphate (MEP) pathway, which was associated with response and induced Vδ2 T cell–mediated antitumor responses in patient-derived tumor organoids. In contrast, riboflavin synthesis was associated with ICB resistance, and its intermediates induced mucosal-associated invariant T (MAIT) cell–mediated immune suppression. Moreover, gut metabolomics revealed that high riboflavin levels were linked to worse survival in patients with abundant intratumoral MAIT cells. Collectively, our results highlight the relevance of metabolite-mediated microbiome–immune cell cross-talk.

Androgen receptor (AR) signaling inhibitors, including enzalutamide, are treatment options for patients with metastatic castration-resistant prostate cancer (mCRPC), but resistance inevitably develops. Using metastatic samples from a prospective phase 2 clinical trial, we epigenetically profile enhancer/promoter activities with acetylation of lysine residue 27 on histone 3 (H3K27ac) chromatin immunoprecipitation followed by sequencing, before and after AR-targeted therapy. We identify a distinct subset of H3K27ac-differentially marked regions that are associated with treatment responsiveness, which we successfully validate in mCRPC patient-derived xenograft (PDX) models. In silico analyses reveal histone deacetylase (HDAC)3 to critically drive resistance to hormonal interventions, which we validate in vitro. Critically, we identify the pan-HDAC inhibitor vorinostat to be effective in decreasing tumor cell proliferation, both in vitro and in vivo. Moreover, we uncover evidence for HDAC3 working together with glucocorticoid receptor (GR) as a potential mechanism for this therapeutic effect. These findings demonstrate the rationale for therapeutic strategies including HDAC inhibitors to improve patient outcome in advanced stages of mCRPC.

Purpose: In NABUCCO, the safety and efficacy of preoperative ipilimumab plus nivolumab were assessed in stage III urothelial cancer. Encouraging responses were achieved, and ipilimumab 3 mg/kg (ipilimumab-high) seemed more effective than ipilimumab 1 mg/kg (ipilimumab-low). We explored ipilimumab plus nivolumab response biomarkers and tumor microenvironment (TME) treatment dynamics. Patients and Methods: Baseline formalin-fixed, paraffinembedded tumor tissue was analyzed using PD-L1 IHC (n ¼ 51) and whole-exome and transcriptome sequencing (both n ¼ 53) and correlated with response. Baseline infiltration of CD8⁺ T cells (n ¼ 51) and at cystectomy (n ¼ 42) was examined. Single-cell RNA sequencing (scRNA-seq) of CD3⁺ T cells was conducted on on-treatment resection tissue of two responders to ipilimumab-high to explore the characteristics of CD8⁺ T cells within the TME. Results: High tumor mutational burden and PD-L1 positivity were associated with response to ipilimumab plus nivolumab. Nonresponding patients exhibited increased expression of a TGFβ signature. We observed increased transcription of the g2m checkpoint and e2f target in responders to ipilimumab-high and enhanced transcription of IFN-α and IFN-γ hallmarks in responders to ipilimumab-low. CD8⁺TCF7⁺ T cells accumulated in the TME of responders to ipilimumab-high. scRNA-seq of CD8A⁺TCF7⁺ T cells demonstrated enhanced expression of IL7R, CCR7, GPR15, XCL1, SELL, and LEF1. Conclusions: Our data indicate that tumor mutational burden, PD-L1, and TGFβ are potential biomarkers for response to ipilimumab plus nivolumab in stage III urothelial cancer. An inflammatory TME might be relevant for responding to ipilimumab-low. We found that in responders to ipilimumabhigh, TCF7⁺CD8⁺ T cells accumulated in the TME. scRNA-seq in two responders suggested that TCF7⁺CD8A⁺ T cells express genes associated with immunologic memory formation and T-cell homing.

Ductal carcinoma in situ (DCIS) may progress to ipsilateral invasive breast cancer (iIBC), but often never will. Because DCIS is treated as early breast cancer, many women with harmless DCIS face overtreatment. To identify features associated with progression, we developed an artificial intelligence-based DCIS morphometric analysis pipeline (AIDmap) on hematoxylin-eosin-stained (H&E) tissue sections. We analyzed 689 digitized H&Es of pure primary DCIS of which 226 were diagnosed with subsequent iIBC and 463 were not. The distribution of 15 duct morphological measurements was summarized in 55 morphometric variables. A ridge regression classifier with cross validation predicted 5-years-free of iIBC with an area-under the curve of 0.67 (95% CI 0.57–0.77). A combined clinical-morphometric signature, characterized by small-sized ducts, a low number of cells and a low DCIS/stroma ratio, was associated with outcome (HR = 0.56; 95% CI 0.28–0.78). AIDmap has potential to identify harmless DCIS that may not need treatment.

Background: Pre-operative immune checkpoint blockade (ICB) with ipilimumab and nivolumab has shown encouraging pathological complete response (pCR) rates in stage III urothelial cancer (UC). A previous analysis of NABUCCO suggested that ipilimumab 3 mg/kg is more effective than ipilimumab 1 mg/kg. However, long-term progression-free and overall survival (PFS, OS) following pre-operative combination ICB are unknown. Methods: In NABUCCO, 54 patients received pre-operative ipilimumab plus nivolumab in different dosing regimens. PFS and OS were determined for the entire NABUCCO population and various clinically relevant subgroups. We explored ICB effects on the cellular composition of tumor-draining lymph nodes (tdLN) from ICB-treated patients (n = 5) and untreated or chemotherapy-treated patients (n = 5) using multiplex immunofluorescence for the PhenoCycler Fusion (Akoya). Results: With a median follow-up of 70 months, PFS and OS at 60 months were 67 % and 70 %, respectively, for the entire study. PFS and OS at 60 months were similar for patients with residual non-muscle invasive UC (NMIBC) and patients with a pCR. The presence of a nodal micrometastasis (<2 mm) after ICB, the development of grade ≥ 3 immune-related adverse events (irAE) and corticosteroids or antibiotics did not negatively impact survival. We observed smaller distances from CD20⁺ cells to CD14⁺ cells in tdLN following ICB compared to tdLN from untreated or chemotherapy-treated patients. Conclusions: Our data demonstrate a 5-year PFS of 67 % and OS of 70 % after pre-operative ICB in stage III UC. Survival was not impaired for patients with residual NMIBC, a nodal micrometastasis at resection, grade ≥ 3 irAE or corticosteroid use.

Objective: To assess the feasibility of scalable, objective, and minimally invasive liquid biopsy-derived biomarkers such as cell-free DNA copy number profiles, human epididymis protein 4 (HE4), and cancer antigen 125 (CA125) for pre-operative risk assessment of early-stage ovarian cancer in a clinically representative and diagnostically challenging population and to compare the performance of these biomarkers with the Risk of Malignancy Index (RMI). Methods: In this case-control study, we included 100 patients with an ovarian mass clinically suspected to be early-stage ovarian cancer. Of these 100 patients, 50 were confirmed to have a malignant mass (cases) and 50 had a benign mass (controls). Using WisecondorX, an algorithm used extensively in non-invasive prenatal testing, we calculated the benign-calibrated copy number profile abnormality score. This score represents how different a sample is from benign controls based on copy number profiles. We combined this score with HE4 serum concentration to separate cases and controls. Results: Combining the benign-calibrated copy number profile abnormality score with HE4, we obtained a model with a significantly higher sensitivity (42% vs 0%; p<0.002) at 99% specificity as compared with the RMI that is currently employed in clinical practice. Investigating performance in subgroups, we observed especially large differences in the advanced stage and non-high-grade serous ovarian cancer groups. Conclusion: This study demonstrates that cell-free DNA can be successfully employed to perform pre-operative risk of malignancy assessment for ovarian masses; however, results warrant validation in a more extensive clinical study.

Microbial communities are resident to multiple niches of the human body and are important modulators of the host immune system and responses to anticancer therapies. Recent studies have shown that complex microbial communities are present within primary tumors. To investigate the presence and relevance of the microbiome in metastases, we integrated mapping and assembly-based metagenomics, genomics, transcriptomics, and clinical data of 4,160 metastatic tumor biopsies. We identified organ-specific tropisms of microbes, enrichments of anaerobic bacteria in hypoxic tumors, associations between microbial diversity and tumor-infiltrating neutrophils, and the association of Fusobacterium with resistance to immune checkpoint blockade (ICB) in lung cancer. Furthermore, longitudinal tumor sampling revealed temporal evolution of the microbial communities and identified bacteria depleted upon ICB. Together, we generated a pan-cancer resource of the metastatic tumor microbiome that may contribute to advancing treatment strategies.

Combining drugs can enhance their clinical efficacy, but the number of possible combinations and inter-tumor heterogeneity make identifying effective combinations challenging, while existing approaches often overlook clinically relevant activity. We screen one of the largest cell line panels (N = 757) with 51 clinically relevant combinations and identify responses at the level of individual cell lines and tissue populations. We establish three response classes to model cellular effects beyond monotherapy: synergy, Bliss additivity, and independent drug action (IDA). Synergy is rare (11% of responses) and frequently efficacious (>50% viability reduction), whereas Bliss and IDA are more frequent but less frequently efficacious. We introduce “efficacious combination benefit” (ECB) to describe high-efficacy responses classified as either synergy, Bliss, or IDA. We identify ECB biomarkers in vitro and show that ECB predicts response in patient-derived xenografts better than synergy alone. Our work here provides a valuable resource and framework for preclinical evaluation and the development of combination treatments.

BACKGROUND Mismatch repair–deficient (dMMR) tumors can be found in 10 to 15% of patients with nonmetastatic colon cancer. In these patients, the efficacy of chemotherapy is limited. The use of neoadjuvant immunotherapy has shown promising results, but data from studies of this approach are limited. METHODS We conducted a phase 2 study in which patients with nonmetastatic, locally advanced, previously untreated dMMR colon cancer were treated with neoadjuvant nivolumab plus ipilimumab. The two primary end points were safety, defined by timely surgery (i.e., ≤2-week delay of planned surgery owing to treatment-related toxic events), and 3-year disease-free survival. Secondary end points included pathological response and results of genomic analyses. RESULTS Of 115 enrolled patients, 113 (98%; 97.5% confidence interval [CI], 93 to 100) underwent timely surgery; 2 patients had surgery delayed by more than 2 weeks. Grade 3 or 4 immune-related adverse events occurred in 5 patients (4%), and none of the patients discontinued treatment because of adverse events. Among the 111 patients included in the efficacy analysis, a pathological response was observed in 109 (98%; 95% CI, 94 to 100), including 105 (95%) with a major pathological response (defined as ≤10% residual viable tumor) and 75 (68%) with a pathological complete response (0% residual viable tumor). With a median follow-up of 26 months (range, 9 to 65), no patients have had recurrence of disease. CONCLUSIONS In patients with locally advanced dMMR colon cancer, neoadjuvant nivolumab plus ipilimumab had an acceptable safety profile and led to a pathological response in a high proportion of patients.

Cells in the tumor microenvironment (TME) influence each other through secretion and sensing of soluble mediators, such as cytokines and chemokines. While signaling of interferon γ (IFNγ) and tumor necrosis factor α (TNFα) is integral to anti-tumor immune responses, our understanding of the spatiotemporal behavior of these cytokines is limited. Here, we describe a single cell transcriptome-based approach to infer which signal(s) an individual cell has received. We demonstrate that, contrary to expectations, CD8⁺ T cell-derived IFNγ is the dominant modifier of the TME relative to TNFα. Furthermore, we demonstrate that cell pools that show abundant IFNγ sensing are characterized by decreased expression of transforming growth factor β (TGFβ)-induced genes, consistent with IFNγ-mediated TME remodeling. Collectively, these data provide evidence that CD8⁺ T cell-secreted cytokines should be categorized into local and global tissue modifiers, and describe a broadly applicable approach to dissect cytokine and chemokine modulation of the TME.

PURPOSE: Platinum-based chemotherapy and immune checkpoint inhibitors are key components of systemic treatment for muscle-invasive and advanced urothelial cancer. The ideal integration of these two treatment modalities remains unclear as clinical trials have led to inconsistent results. Modulation of the tumor-immune microenvironment by chemotherapy is poorly characterized. We aimed to investigate this modulation, focusing on potential clinical implications for immune checkpoint inhibitor response. EXPERIMENTAL DESIGN: We assessed immune cell densities, spatial relations, and tumor/stromal components from 116 patients with urothelial bladder cancer (paired data for 95 patients) before and after platinum-based chemotherapy. RESULTS: Several published biomarkers for immunotherapy response changed upon chemotherapy treatment. The intratumoral CD8+ T-cell percentage increased after treatment and was associated with increased TNFα-via-NF-κB signaling. The percentage of PDL1+ immune cells was higher after chemotherapy. An increase in chemo-induced changes that potentially inhibit an antitumor immune response was also observed, including increased fibroblast-based TGFβ signaling and distances from immune cells to the nearest cancer cell. The latter two parameters correlated significantly in posttreatment samples, suggesting that TGFβ signaling in fibroblasts may play a role in spatially separating immune cells from cancer cells. We examined specific chemotherapy regimens and found that treatment with methotrexate, vinblastine, doxorubicin, and cisplatin was associated with an increase in the macrophage cell percentage. Gemcitabine-containing chemotherapy was associated with upregulation of fibroblast TGFβ signaling. CONCLUSIONS: The opposing effects of platinum-based chemotherapy on the immune cell composition and stromal context of the tumor-immune microenvironment may explain the inconsistent results of clinical trials investigating chemotherapy and immune checkpoint inhibitor combinations in bladder cancer.

Immune checkpoint inhibitors (ICI) can achieve remarkable responses in urothelial cancer (UC), which may depend on tumor microenvironment (TME) characteristics. However, the relationship between the TME, usually characterized by immune cell density, and response to ICI is unclear. Here, we quantify the TME immune cell densities and spatial relationships (SRs) of 24 baseline UC samples, obtained before pre-operative combination ICI treatment, using multiplex immunofluorescence. We describe SRs by approximating the first nearest-neighbor distance distribution with a Weibull distribution and evaluate the association between TME metrics and ipilimumab+nivolumab response. Immune cell density does not discriminate between response groups. However, the Weibull SR metrics of CD8⁺ T cells or macrophages to their closest cancer cell positively associate with response. CD8⁺ T cells close to B cells are characteristic of non-response. We validate our SR response associations in a combination ICI cohort of head and neck tumors. Our data confirm that SRs, in contrast to density metrics, are strong biomarkers of response to pre-operative combination ICIs.

The primary objective of the prospective, randomized, multicenter, phase 3 biomarker Microarray Analysis in breast cancer to Taylor Adjuvant Drugs Or Regimens trial (MATADOR: ISRCTN61893718) is to generate a gene expression profile that can predict benefit from either docetaxel, doxorubicin, and cyclophosphamide (TAC) or dose-dense scheduled doxorubicin and cyclophosphamide (ddAC). Patients with a pT1-3, pN0-3 tumor were randomized 1:1 between ddAC and TAC. The primary endpoint was a gene profile-treatment interaction for recurrence-free survival (RFS). We observed 117 RFS events in 664 patients with a median follow-up of 7 years. Hallmark gene set analyses showed significant association between enrichment in immune-related gene expression and favorable outcome after TAC in hormone receptor-negative, human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC) (triple-negative breast cancer [TNBC]). We validated this association in TNBC patients treated with TAC on H&E slides; stromal tumor-infiltrating lymphocytes (sTILs) ≥20% was associated with longer RFS (hazard ratio 0.18, p = 0.01), while in patients treated with ddAC no difference in RFS was seen (hazard ratio 0.92, p = 0.86, p_interaction = 0.02).

PURPOSE: The clinical value of STK11, KEAP1, and EGFR alterations for guiding immune checkpoint blockade (ICB) therapy in non-small cell lung cancer (NSCLC) remains controversial, as some patients with these proposed resistance biomarkers show durable ICB responses. More specific combinatorial biomarker approaches are urgently needed for this disease. EXPERIMENTAL DESIGN: To develop a combinatorial biomarker strategy with increased specificity for ICB unresponsiveness in NSCLC, we performed a comprehensive analysis of 254 patients with NSCLC treated with ligand programmed death-ligand 1 (PD-L1) blockade monotherapy, including a discovery cohort of 75 patients subjected to whole-genome sequencing (WGS), and an independent validation cohort of 169 patients subjected to tumor-normal large panel sequencing. The specificity of STK11/KEAP1/EGFR alterations for ICB unresponsiveness was assessed in the contexts of a low (<10 muts/Mb) or high (≥10 muts/Mb) tumor mutational burden (TMB). RESULTS: In low TMB cases, STK11/KEAP1/EGFR alterations were highly specific biomarkers for ICB resistance, with 0/15 (0.0%) and 1/34 (2.9%) biomarker-positive patients showing treatment benefit in the discovery and validation cohorts, respectively. This contrasted with high TMB cases, where 11/13 (85%) and 15/34 (44%) patients with at least one STK11/KEAP1/EGFR alteration showed durable treatment benefit in the discovery and validation cohorts, respectively. These findings were supported by analyses of progression-free survival and overall survival. CONCLUSIONS: The unexpected ICB responses in patients carrying resistance biomarkers in STK11, KEAP1, and EGFR were almost exclusively observed in patients with a high TMB. Considering these alterations in context, the TMB offered a highly specific combinatorial biomarker strategy for limiting overtreatment in NSCLC.

In this article the data availability statement was incorrectly given. “EGAS0000100682” should have been “EGAS00001006821”. The correct statement as follows. The datasets generated during and/or analysed during the current study are deposited at the European Genome-phenome Archive (EGA) under the accession numbers EGAS00001006821 and EGAD00001009790. The original article has been corrected.

Genomics has greatly improved how patients with cancer are being treated; however, clinical-grade genomic biomarkers for chemotherapies are currently lacking. Using whole-genome analysis of 37 patients with metastatic colorectal cancer (mCRC) treated with the chemotherapy trifluridine/tipiracil (FTD/TPI), we identified KRAS codon G12 (KRAS^G12) mutations as a potential biomarker of resistance. Next, we collected real-world data of 960 patients with mCRC receiving FTD/TPI and validated that KRAS^G12 mutations were significantly associated with poor survival, also in analyses restricted to the RAS/RAF mutant subgroup. We next analyzed the data of the global, double-blind, placebo-controlled, phase 3 RECOURSE trial (n = 800 patients) and found that KRAS^G12 mutations (n = 279) were predictive biomarkers for reduced overall survival (OS) benefit of FTD/TPI versus placebo (unadjusted interaction P = 0.0031, adjusted interaction P = 0.015). For patients with KRAS^G12 mutations in the RECOURSE trial, OS was not prolonged with FTD/TPI versus placebo (n = 279; hazard ratio (HR) = 0.97; 95% confidence interval (CI) = 0.73–1.20; P = 0.85). In contrast, patients with KRAS^G13 mutant tumors showed significantly improved OS with FTD/TPI versus placebo (n = 60; HR = 0.29; 95% CI = 0.15–0.55; P < 0.001). In isogenic cell lines and patient-derived organoids, KRAS^G12 mutations were associated with increased resistance to FTD-based genotoxicity. In conclusion, these data show that KRAS^G12 mutations are biomarkers for reduced OS benefit of FTD/TPI treatment, with potential implications for approximately 28% of patients with mCRC under consideration for treatment with FTD/TPI. Furthermore, our data suggest that genomics-based precision medicine may be possible for a subset of chemotherapies.

BACKGROUND: High-dimensional prediction considers data with more variables than samples. Generic research goals are to find the best predictor or to select variables. Results may be improved by exploiting prior information in the form of co-data, providing complementary data not on the samples, but on the variables. We consider adaptive ridge penalised generalised linear and Cox models, in which the variable-specific ridge penalties are adapted to the co-data to give a priori more weight to more important variables. The R-package ecpc originally accommodated various and possibly multiple co-data sources, including categorical co-data, i.e. groups of variables, and continuous co-data. Continuous co-data, however, were handled by adaptive discretisation, potentially inefficiently modelling and losing information. As continuous co-data such as external p values or correlations often arise in practice, more generic co-data models are needed. RESULTS: Here, we present an extension to the method and software for generic co-data models, particularly for continuous co-data. At the basis lies a classical linear regression model, regressing prior variance weights on the co-data. Co-data variables are then estimated with empirical Bayes moment estimation. After placing the estimation procedure in the classical regression framework, extension to generalised additive and shape constrained co-data models is straightforward. Besides, we show how ridge penalties may be transformed to elastic net penalties. In simulation studies we first compare various co-data models for continuous co-data from the extension to the original method. Secondly, we compare variable selection performance to other variable selection methods. The extension is faster than the original method and shows improved prediction and variable selection performance for non-linear co-data relations. Moreover, we demonstrate use of the package in several genomics examples throughout the paper. CONCLUSIONS: The R-package ecpc accommodates linear, generalised additive and shape constrained additive co-data models for the purpose of improved high-dimensional prediction and variable selection. The extended version of the package as presented here (version number 3.1.1 and higher) is available on ( https://cran.r-project.org/web/packages/ecpc/ ).

The authors regret that the following statement regarding author contributions was missed: Kristan van der Vos is currently a Scientific Editor for Cell Reports Medicine, which is published by Elsevier. Dr van der Vos was not involved in the peer-review process or editorial discussions about this manuscript. The authors would like to apologise for any inconvenience caused.

Background: Studies have shown that blood platelets contain tumour-specific mRNA profiles tumour-educated platelets (TEPs). Here, we aim to train a TEP-based breast cancer detection classifier. Methods: Platelet mRNA was sequenced from 266 women with stage I–IV breast cancer and 212 female controls from 6 hospitals. A particle swarm optimised support vector machine (PSO-SVM) and an elastic net-based classifier (EN) were trained on 71% of the study population. Classifier performance was evaluated in the remainder (29%) of the population, followed by validation in an independent set (37 cases and 36 controls). Potential confounding was assessed in post hoc analyses. Results: Both classifiers reached an area under the curve (AUC) of 0.85 upon internal validation. Reproducibility in the independent validation set was poor with an AUC of 0.55 and 0.54 for the PSO-SVM and EN classifier, respectively. Post hoc analyses indicated that 19% of the variance in gene expression was associated with hospital. Genes related to platelet activity were differentially expressed between hospitals. Conclusions: We could not validate two TEP-based breast cancer classifiers in an independent validation cohort. The TEP protocol is sensitive to within-protocol variation and revision might be necessary before TEPs can be reconsidered for breast cancer detection.