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.

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.

Cisplatin-based neoadjuvant chemotherapy (NAC) followed by radical cystectomy is recommended for patients with muscle-invasive bladder cancer (MIBC). It has been shown that somatic deleterious mutations in ERCC2, gain-of-function mutations in ERBB2, and alterations in ATM, RB1, and FANCC are correlated with pathological response to NAC in MIBC. The objective of this study was to validate these genomic biomarkers in pretreatment transurethral resection material from an independent retrospective cohort of 165 patients with MIBC who subsequently underwent NAC and radical surgery. Patients with ypT0/Tis/Ta/T1N0 disease after surgery were defined as responders. Somatic deleterious mutations in ERCC2 were found in nine of 68 (13%) evaluable responders and two of 95 (2%) evaluable nonresponders (p = 0.009; FDR = 0.03). No correlation was observed between response and alterations in ERBB2 or in ATM, RB1, or FANCC alone or in combination. In an exploratory analysis, no additional genomic alterations discriminated between responders and nonresponders to NAC. No further associations were identified between the aforementioned biomarkers and pathological complete response (ypT0N0) after surgery. In conclusion, we observed a positive association between deleterious mutations in ERCC2 and pathological response to NAC, but not overall survival or recurrence-free survival. Other previously reported genomic biomarkers were not validated. Patient summary: It is currently unknown which patients will respond to chemotherapy before definitive surgery for bladder cancer. Previous studies described several gene mutations in bladder cancer that correlated with chemotherapy response. This study confirmed that patients with bladder cancer with a mutation in the ERCC2 gene often respond to chemotherapy.

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.

Combinations of anti-cancer drugs can overcome resistance and provide new treatments^1,2. The number of possible drug combinations vastly exceeds what could be tested clinically. Efforts to systematically identify active combinations and the tissues and molecular contexts in which they are most effective could accelerate the development of combination treatments. Here we evaluate the potency and efficacy of 2,025 clinically relevant two-drug combinations, generating a dataset encompassing 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines. We show that synergy between drugs is rare and highly context-dependent, and that combinations of targeted agents are most likely to be synergistic. We incorporate multi-omic molecular features to identify combination biomarkers and specify synergistic drug combinations and their active contexts, including in basal-like breast cancer, and microsatellite-stable or KRAS-mutant colon cancer. Our results show that irinotecan and CHEK1 inhibition have synergistic effects in microsatellite-stable or KRAS–TP53 double-mutant colon cancer cells, leading to apoptosis and suppression of tumour xenograft growth. This study identifies clinically relevant effective drug combinations in distinct molecular subpopulations and is a resource to guide rational efforts to develop combinatorial drug treatments.

Purpose: Previously, we developed breast cancer BRCA1-like and BRCA2-like copy-number profile shrunken centroid classifiers predictive for mutation status and response to therapy, targeting homologous recombination deficiency (HRD). Therefore, we investigated BRCA1- and BRCA2-like classification in ovarian cancer, aiming to acquire classifiers with similar properties as those in breast cancer. Experimental Design: We analyzed DNA copy-number profiles of germline BRCA1- and BRCA2-mutant ovarian cancers and control tumors and observed that existing breast cancer classifiers did not sufficiently predict mutation status. Hence, we trained new shrunken centroid classifiers on this set and validated them in the independent The Cancer Genome Atlas dataset. Subsequently, we assessed BRCA1/2-like classification and obtained germline and tumor mutation and methylation status of cancer predisposition genes, among them several involved in HR repair, of 300 ovarian cancer samples derived from the consecutive cohort trial AGO-TR1 (NCT02222883). Results: The detection rate of the BRCA1-like classifier for BRCA1 mutations and promoter hypermethylation was 95.6%. The BRCA2-like classifier performed less accurately, likely due to a smaller training set. Furthermore, three quarters of the BRCA1/ 2-like tumors could be explained by (epi)genetic alterations in BRCA1/2, germline RAD51C mutations and alterations in other genes involved in HR. Around half of the non-BRCA-mutated ovarian cancer cases displayed a BRCA-like phenotype. Conclusions: The newly trained classifiers detected most BRCAmutated and methylated cancers and all tumors harboring a RAD51C germline mutations. Beyond that, we found an additional substantial proportion of ovarian cancers to be BRCA-like. _2021 The Authors; Published by the American Association for Cancer Research.

Candidate immune biomarkers have been proposed for predicting response to immunotherapy in urothelial cancer (UC). Yet, these biomarkers are imperfect and lack predictive power. A comprehensive overview of the tumor immune contexture, including Tertiary Lymphoid structures (TLS), is needed to better understand the immunotherapy response in UC. We analyzed tumor sections by quantitative multiplex immunofluorescence to characterize immune cell subsets in various tumor compartments in tumors without pretreatment and tumors exposed to preoperative anti-PD1/CTLA-4 checkpoint inhibitors (NABUCCO trial). Pronounced immune cell presence was found in UC invasive margins compared to tumor and stroma regions. CD8⁺PD1⁺ T-cells were present in UC, particularly following immunotherapy. The cellular composition of TLS was assessed by multiplex immunofluorescence (CD3, CD8, FoxP3, CD68, CD20, PanCK, DAPI) to explore specific TLS clusters based on varying immune subset densities. Using a k-means clustering algorithm, we found five distinct cellular composition clusters. Tumors unresponsive to anti-PD-1/CTLA-4 immunotherapy showed enrichment of a FoxP3⁺ T-cell-low TLS cluster after treatment. Additionally, cluster 5 (macrophage low) TLS were significantly higher after pre-operative immunotherapy, compared to untreated tumors. We also compared the immune cell composition and maturation stages between superficial (submucosal) and deeper TLS, revealing that superficial TLS had more pronounced T-helper cells and enrichment of early TLS than TLS located in deeper tissue. Furthermore, superficial TLS displayed a lower fraction of secondary follicle like TLS than deeper TLS. Taken together, our results provide a detailed quantitative overview of the tumor immune landscape in UC, which can provide a basis for further studies.

Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.

Integrative analyses that summarize and link molecular data to treatment sensitivity are crucial to capture the biological complexity which is essential to further precision medicine. We introduce Weighted Orthogonal Nonnegative parallel factor analysis (WON-PARAFAC), a data integration method that identifies sparse and interpretable factors. WON-PARAFAC summarizes the GDSC1000 cell line compendium in 130 factors. We interpret the factors based on their association with recurrent molecular alterations, pathway enrichment, cancer type, and drug-response. Crucially, the cell line derived factors capture the majority of the relevant biological variation in Patient-Derived Xenograft (PDX) models, strongly suggesting our factors capture invariant and generalizable aspects of cancer biology. Furthermore, drug response in cell lines is better and more consistently translated to PDXs using factor-based predictors as compared to raw feature-based predictors. WON-PARAFAC efficiently summarizes and integrates multiway high-dimensional genomic data and enhances translatability of drug response prediction from cell lines to patient-derived xenografts.