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Multiplexed imaging technologies enable the study of biological tissues at single-cell resolution while preserving spatial information. Currently, high-dimension imaging data analysis is technology-specific and requires multiple tools, restricting analytical scalability and result reproducibility. Here we present SIMPLI (Single-cell Identification from MultiPLexed Images), a flexible and technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After raw image processing, SIMPLI performs a spatially resolved, single-cell analysis of the tissue slide as well as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for large datasets. SIMPLI produces multiple tabular and graphical outputs at each step of the analysis. Its containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. Software is available at “SIMPLI [ https://github.com/ciccalab/SIMPLI ]”. Current high-dimension imaging data analysis methods are technology-specific and require multiple tools, restricting analytical scalability and result reproducibility. Here the authors present SIMPLI, a software that overcomes these limitations for single-cell and pixel analysis of multiplexed images at spatial resolution.

Background Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development. Results Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Conclusions Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/ .

Background The crosstalk between cancer and the tumour immune microenvironment (TIME) has attracted significant interest in the latest years because of its impact on cancer evolution and response to treatment. Despite this, cancer-specific tumour-TIME interactions and their mechanistic insights are still poorly understood. Methods Here, we compute the significant interactions occurring between cancer-specific genetic drivers and five anti- and pro-tumour TIME features in 32 cancer types using Lasso regularised ordinal regression. Focusing on head and neck squamous cancer (HNSC), we rebuild the functional networks linking specific TIME driver alterations to the TIME state they associate with. Results The 477 TIME drivers that we identify are multifunctional genes whose alterations are selected early in cancer evolution and recur across and within cancer types. Tumour suppressors and oncogenes have an opposite effect on the TIME and the overall anti-tumour TIME driver burden is predictive of response to immunotherapy. TIME driver alterations predict the immune profiles of HNSC molecular subtypes, and perturbations in keratinization, apoptosis and interferon signalling underpin specific driver-TIME interactions. Conclusions Overall, our study delivers a comprehensive resource of TIME drivers, gives mechanistic insights into their immune-regulatory role, and provides an additional framework for patient prioritisation to immunotherapy. The full list of TIME drivers and associated properties are available at http://www.network-cancer-genes.org .

The tumor microenvironment (TME) is highly heterogeneous and can dictate the success of therapeutic interventions. Identifying TMEs that are susceptible to specific therapeutic interventions paves the way for more personalized and effective treatments. In this study, using a spontaneous murine model of breast cancer, we characterize a TME that is responsive to inhibitors of the heme degradation pathway mediated by heme oxygenase (HO), resulting in CD8+ T cell- and NK cell-dependent tumor control. A hallmark of this TME is a chronic type I interferon (IFN) signal that is directly involved in orchestrating the antitumor immune response. Importantly, we identify that similar TMEs exist in human breast cancer that are associated with patient prognosis. Leveraging these observations, we demonstrate that combining a STING agonist, which induces type I IFN responses, with an HO inhibitor produces a synergistic effect leading to superior tumor control. This study highlights HO activity as a potential resistance mechanism for type I IFN responses in cancer, supporting a therapeutic rationale for targeting the heme degradation pathway to enhance the efficacy of STING agonists.

BackgroundWhy some patients fail or have short lived response to immune checkpoint blockade (ICB) immunotherapy remains largely unknown. While baseline molecular assessments have provided clues to prognostic factors, insights into resistance drivers remains elusive. This is partially due to the difficulty in getting access to post progression samples from patients that were either primary resistant or developed acquired resistance after an initial response to ICB. Thus, the tumour-intrinsic and -extrinsic features that are selected for during progression and potentially drive primary and acquired resistance to immunotherapy remain underexplored.MethodsTo compare clinical features and immunogenomic drivers of acquired and primary resistance to ICB across major cancers, we analysed and annotated de-identified patient records in the Tempus real-world database1 2 (figure 1). We built an immuno-oncology cohort consisting of >2500 multimodal (DNA, RNA and clinical outcome data) pre-treatment baseline with >1500 post-treatment tumour biopsy samples from mainly NSCLC, TNBC, HNC and Bladder cancer patients. We used bulk RNA-seq data to estimate activation of the hallmark oncogenic pathways3 and immune cell composition4 and used panel DNA-seq data (>500 genes) to quantify mutation selection at the gene and pathway levels using dndscv.5 ResultsCompared to acquired, primary resistant patients tended to have a higher observation of liver lesions at progression. Post-ICB, acquired resistant NSCLC and HNC patients showed a significantly inflamed tumour microenvironment (TME) characterised by higher estimation of infiltration of T cells and myeloid cells and higher activation of interferon gamma (IFNg) signalling as compared to primary resistant patients. In addition, in post-ICB acquired resistance in NSCLC we observed selection for mutations in genes involved in known immunomodulatory pathways, including loss-of-function mutations in B2M in the antigen processing and presentation machinery (APM) pathway and APC in the Wnt pathway. Consistently, acquired resistance patients showed stronger selection for mutations in APM, IFN, WNT, MYC, and Notch pathways as compared to primary resistance patients across NSCLC, HNC and bladder cancer post-ICB.ConclusionsAcquired and primary ICB resistant patients have distinct clinical and molecular features at progression. Their tumours’ TME is fundamentally different with acquired resistance TMEs being infiltrated with immune cells albeit escaped post progression. In addition, ICB selects mutations that potentially activate immunosuppressive pathways such as Wnt and Myc. This multi-modal Real-World Data with post therapy biopsies has given insights for patient selection strategies and provides rational into combination treatment options for acquired resistant patients.References1. Fernandes LE, Epstein CG, Bobe AM, Bell JSK, Stumpe MC, Salazar ME, Salahudeen AA, Pe Benito RA, McCarter C, Leibowitz BD, Kase M, Igartua C, Huether R, Hafez A, Beaubier N, Axelson MD, Pegram MD, Sammons SL, O’Shaughnessy JA, Palmer GA. Real-world evidence of diagnostic testing and treatment patterns in US patients with breast cancer with implications for treatment biomarkers from RNA sequencing data. Clin Breast Cancer. 2021 Aug;21(4):e340-e361. doi: 10.1016/j.clbc.2020.11.012. Epub 2020 Dec 18. PMID: 33446413.2. Rivera DR, Henk HJ, Garrett-Mayer E, Christian JB, Belli AJ, Bruinooge SS, Espirito JL, Sweetnam C, Izano MA, Natanzon Y, Robert NJ, Walker MS, Cohen AB, Boyd M, Enewold L, Hansen E, Honnold R, Kushi L, Mishra Kalyani PS, Pe Benito R, Sakoda LC, Sharon E, Tymejczyk O, Valice E, Wagner J, Lasiter L, Allen JD. The friends of cancer research real-world data collaboration pilot 2.0: methodological recommendations from oncology case studies. Clin Pharmacol Ther. 2022 Jan;111(1):283–292. doi: 10.1002/cpt.2453. Epub 2021 Nov 11. PMID: 34664259; PMCID: PMC9298732.3. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 2015 Dec 23;1(6):417–425. doi: 10.1016/j.cels.2015.12.004. PMID: 26771021; PMCID: PMC4707969.4. Jiménez-Sánchez A, Cast O, Miller ML. Comprehensive benchmarking and integration of tumor microenvironment cell estimation methods. Cancer Res. 2019 Dec 15;79(24):6238–6246. doi: 10.1158/0008–5472.CAN-18–3560. Epub 2019 Oct 22. PMID: 31641033.5. Martincorena I, Raine KM, Gerstung M, Dawson KJ, Haase K, Van Loo P, Davies H, Stratton MR, Campbell PJ. Universal patterns of selection in cancer and somatic tissues. Cell. 2017 Nov 16;171(5):1029–1041.e21. doi: 10.1016/j.cell.2017.09.042. Epub 2017 Oct 19. Erratum in: Cell. 2018 Jun 14;173(7):1823. PMID: 29056346; PMCID: PMC5720395.Ethics ApprovalAll ethics and consent have been obtained in accordance with Tempus Labs IRB approval supporting the use of de-identified data.ConsentAll ethics and consent have been obtained in accordance with Tempus Labs IRB approval supporting the use of de-identified data.Abstract 620 Figure 1Clinical and immunogenomic features of acquired and primary resistance to ICB

For patients with non-small-cell lung cancer (NSCLC) tumors without currently targetable molecular alterations, standard-of-care treatment is immunotherapy with anti-PD-(L)1 checkpoint inhibitors, alone or with platinum-doublet therapy. However, not all patients derive durable benefit and resistance to immune checkpoint blockade is common. Understanding mechanisms of resistance—which can include defects in DNA damage response and repair pathways, alterations or functional mutations in STK11 /LKB1, alterations in antigen-presentation pathways, and immunosuppressive cellular subsets within the tumor microenvironment—and developing effective therapies to overcome them, remains an unmet need. Here the phase 2 umbrella HUDSON study evaluated rational combination regimens for advanced NSCLC following failure of anti-PD-(L)1-containing immunotherapy and platinum-doublet therapy. A total of 268 patients received durvalumab (anti-PD-L1 monoclonal antibody)–ceralasertib (ATR kinase inhibitor), durvalumab–olaparib (PARP inhibitor), durvalumab–danvatirsen (STAT3 antisense oligonucleotide) or durvalumab–oleclumab (anti-CD73 monoclonal antibody). Greatest clinical benefit was observed with durvalumab–ceralasertib; objective response rate (primary outcome) was 13.9% (11/79) versus 2.6% (5/189) with other regimens, pooled, median progression-free survival (secondary outcome) was 5.8 (80% confidence interval 4.6–7.4) versus 2.7 (1.8–2.8) months, and median overall survival (secondary outcome) was 17.4 (14.1–20.3) versus 9.4 (7.5–10.6) months. Benefit with durvalumab–ceralasertib was consistent across known immunotherapy-refractory subgroups. In ATM -altered patients hypothesized to harbor vulnerability to ATR inhibition, objective response rate was 26.1% (6/23) and median progression-free survival/median overall survival were 8.4/22.8 months. Durvalumab–ceralasertib safety/tolerability profile was manageable. Biomarker analyses suggested that anti-PD-L1/ATR inhibition induced immune changes that reinvigorated antitumor immunity. Durvalumab–ceralasertib is under further investigation in immunotherapy-refractory NSCLC. ClinicalTrials.gov identifier: NCT03334617 In the phase 2 HUDSON study, patients with advanced non-small-cell lung cancer received anti-PD-L1 combined with biomarker-guided therapy targeting ATR kinase, PARP, STAT3 or CD73, leading to encouraging clinical benefit in response to combination of the ATR kinase inhibitor ceralasertib with durvalumab.

The crosstalk between cancer and the tumour immune microenvironment (TIME) has attracted significant interest because of its impact on cancer evolution and response to treatment. Despite this, cancer-specific tumour-TIME interactions and their mechanisms of action are still poorly understood. Here we identified the interactions between cancer-specific genetic drivers and anti- or pro-tumour TIME features in individual samples of 32 cancer types. The resulting 477 TIME drivers are multifunctional genes whose alterations are selected early in cancer evolution and recur across and within cancer types. Moreover, the anti-tumour TIME driver burden is predictive of overall response to immunotherapy. Focusing on head and neck squamous cancer (HNSC), we rebuilt the functional networks linking specific TIME driver alterations to the TIME state. We showed that TIME driver alterations predict the immune profiles of HNSC molecular subtypes, and that deregulation of keratinization, apoptosis and interferon signalling underpin specific driver-TIME interactions. Overall, our study provides a comprehensive resource of TIME drivers giving mechanistic insights into their immune-regulatory role.Competing Interest StatementThe authors have declared no competing interest.

ABSTRACT Colorectal cancers (CRCs) show variable response to immune checkpoint blockade, which can only partially be explained by the variability of tumour mutational burden. To dissect the cellular and molecular determinants of response we performed a multi-omic screen of 721 cancer regions from patients treated with Pembrolizumab (KEYNOTE 177 clinical trial) or Nivolumab. Multi-regional whole exome, RNA and T-cell receptor sequencing show that, within hypermutated CRCs, response to both anti-PD1 agents is not positively associated with tumour mutational burden but with high clonality of immunogenic mutations, expanded T cells, low activation of the WNT pathway and active immune escape mechanisms. Coupling high-dimensional imaging mass cytometry with multiplexed immunofluorescence and computational spatial analysis, we observe that responsive hypermutated CRCs are rich in cytotoxic and proliferating PD1-expressing CD8 cells interacting with high-density clusters of PDL1-expressing antigen presenting macrophages. We propose that anti-PD1 agents release the PD1-PDL1 interaction between CD8 T cells and macrophages thus promoting cytotoxic anti-tumour activity. Competing Interest Statement The authors have declared no competing interest. Footnotes * ↵** email to: jo.spencer{at}kcl.ac.uk; francesca.ciccarelli{at}crick.ac.uk * The authors declare no potential conflicts of interest

Immune checkpoint blockade (ICB) has revolutionised cancer therapy, yet resistance — both primary and acquired — remains a significant obstacle, affecting the majority of patients. Here, we leveraged a large-scale, real-world clinicogenomic dataset to systematically explore the molecular underpinnings of ICB resistance in the post-progression setting. Analysing over 5,000 pan-cancer patients with clinical and pre-/post-treatment genomic and transcriptomic data, we identify distinct immunogenomic drivers of acquired vs. primary ICB resistance. Post-ICB progression, acquired resistance showed extended survival compared to primary resistance across all cancer types. The acquired resistance clinical phenotype was paralleled by a universally immune-inflamed, albeit dysfunctional, tumour microenvironment (TME) at the onset of acquired resistance, with sustained or ICB-induced inflammatory and interferon responses. We confirm previously described mechanisms of acquired resistance, including B2M loss-of-function (LoF) in non-small cell lung cancer (NSCLC), and identify novel potential mediators, including LoF of TGFBR2 in NSCLC, CYLD in head and neck cancer, and RUNX1 in triple negative breast cancer. Further supporting their involvement in resistance, these acquired ICB alterations associated with immune-escaped TMEs, characterised by active immunomodulatory oncogenic signalling, hyperproliferation and invasiveness, or altered tumour metabolism. These findings emphasise the heterogeneity of molecular drivers of acquired resistance to ICB within and across cancers, and highlight the potential for personalised therapeutic interventions post-progression to improve patient outcomes.

Multiplexed imaging technologies enable to study biological tissues at single-cell resolution while preserving spatial information. Currently, the analysis of these data is technology-specific and requires multiple tools, restricting the scalability and reproducibility of results. Here we present SIMPLI (Single-cell Identification from MultiPlexed Images), a novel, technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After processing raw images, SIMPLI performs a spatially resolved, single-cell analysis of the tissue as wells as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for the analysis of large datasets. It produces multiple outputs at each step, including tabular text files and visualisation plots. The containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. SIMPLI is available at:https://github.com/ciccalab/SIMPLI Competing Interest Statement The authors have declared no competing interest. Footnotes * To further clarify the extent of methodological advance and the advantages of SIMPLI compared to existing counterparts, we revised Introduction, Table 1, Fig.1, Supplementary Fig.1. We compared the analysis of normalised and raw images, p.11-12, Supplementary figure 2. We reassigned cells in the lamina propria varying the mask overlap, p.12, Supplementary figure 2. We added another segmentation method and compared the results with the one already implemented in SIMPLI, p.17, Supplementary figure 3. We compared the cell phenotypes from unsupervised clustering at various resolution as well as with those from thresholding, p.17-18, Supplementary figure 3. We repeated the spatial analysis between PD1+CD8+ T cells and PDL1+CD68+ macrophages after re-identifying the latter with the thresholding approach, p.21-22. We revised the heterotypic spatial analysis using more restrictive cut-offs and adding a permutation test to strengthen the results, p.27. We expanded and clarified the modularity in the choice of analysis methods in terms of: Cell segmentation (conventional vs deep learning), Phenotyping (unsupervised vs thresholding), Spatial analysis (homotypic vs heterotypic). These are now described in the text (p.7-9) as well as in the revised Figures 1 and S1. We added further recommendations on the parameter choice in the software documentation and in the Methods and discussed the method's limitations (p.31). Finally, we added the Data Availability and Code Availability sections (p. 42), deposited all new data in Zenodo (Access Codes provided in the text). * https://github.com/ciccalab/SIMPLI