Immune checkpoint inhibitor (ICI) modality has had a limited success (<20%) in treating metastatic recurrent Head & Neck Oropharyngeal Squamous cell carcinomas (OPSCCs). To improve response rates to ICIs, tailored approaches capable to capture the tumor complexity and dynamics of each patient's disease are needed. Here, we performed advanced analyses of spatial proteogenomic technologies to demonstrate that: (i) compared to standard histopathology, spatial transcriptomics better-identified tumor cells and could specifically classify them into two different metabolic states with therapeutic implications; (ii) our new method (Spatial Proteomics-informed cell deconvolution method or SPiD) improved profiling of local immune cell types relevant to disease progression, (iii) identified clinically relevant alternative treatments and a rational explanation for checkpoint inhibitor therapy failure through comparative analysis of pre- and post-failure tumor data and, (iv) discovered ligand-receptor interactions as potential lead targets for personalized drug treatments. Our work establishes a clear path for incorporating spatial-omics in clinical settings to facilitate treatment personalization.
Spatial omics technologies are producing an unprecedented amount of ultra-high plex in situ data that are promising to revolutionize cancer prognoses and treatments. Analytical solutions to integrate big spatial data are, however, lagging relative to the rapid technology development and this hinders discoveries into the pathological processes underlying cancer initiation and progression. Here we present STimage, a machine learning approach to flexibly combine transcriptome-wide spatial sequencing data with single-cell protein-based spatial phenotyping (Phenocycler-Fusion), generated from same tissue samples. As a ground-truth for cell typing, Akoya’s single-cell spatial phenotyping technology enabled us to precisely define cell types and cell states that were then used to evaluate and deploy the data integration pipeline. With these data, STimage first maps cells onto tissue sections with reference an H&E image. This way multiple layers of molecular data are transferred into one common framework, which can then be analyzed together. Traditional pathology annotations on the H&E images are also integrated to add human understanding of morphological patterns in a cancer tissue. The integrated analysis improves cell neighborhood identification, which allows cell-cell interaction analysis based on spatial co-localization between cell types (using single-cell resolution protein data) and locally co-expressing ligand-receptor pairs (using transcriptome-wide spatial data). We applied STimage to head and neck and skin cancer samples, in order to demonstrate the broad applicability of this analysis pipeline for various cancer types. We demonstrate applications for both, diagnoses and prognoses. For diagnosis, ST image identified and cross-validated cell types whilst assessing the expression of markers for drug targets. We also present an in-depth case study for an oropharyngeal squamous cell carcinoma patient not responding to Nivolumab treatment (anti-PD-1). Based on Visium and PhenoCycler-Fusion data, we discovered a spatial signature for non-responsiveness. Furthermore, we used the predicted ligand-receptor interactions to rank the patient’s response potential to currently available drugs, with the top target being TF-TFRC. STimage is thus able to integrate multiple layers of spatial omics data to improve and solidify prognostic biomarkers for cancer treatments. This study highlights the power of machine learning integration to combine multiple spatial multi-omics data, in particular PhenoCycler-Fusion and Visium, for improving diagnosis, prognosis and treatment of diverse cancers. Citation Format: Xiao Tan, Andrew Causer, Jazmina Gonzales-Cruz, Ning Ma, Bassem Ben Cheikh, Oliver Braubach, Quan Nguyen. Machine learning integration of transcriptome-wide spatial sequencing data and ultra-high plex spatial proteomic data enables the prioritization of cancer drug targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2059.
Cancer responses to drug treatment are highly heterogeneous. We postulate that spatial determinants in the tumour play a critical role in cancer therapy outcomes. Here, we will present two spatial transcriptomics studies on spatial responses to immunotherapy and chemotherapy. Immune checkpoint inhibitors (ICI) are used to treat recurrent metastatic oropharyngeal squamous cell carcinomas (OPSCC). Unfortunately, less than 30% of patients benefit from this therapy. Thus, we performed spatial transcriptomics (ST) and in-situ multiprotein detection (PhenoCycler-Fusion) on tissue isolated from a patient diagnosed with metastatic OPSCC. The patient’s primary oral tumour responded to chemo-radio therapy, followed by nivolumab ICI. However, new soft pallet OPSCCs resurged. Subsequent pembrolizumab combined with lenvatinib (VEGFR inhibitor) treatment had an initial effect, butnew recurrent oral tumours re-emerged suggesting drug resistance. Using ST, we observed high expression of drug resistance genes such as SNAI2, SOX4 and NDRG1 consistent with the disease aggressive behaviour. Although, PD-1/PD-L1 expression was not observed, we identified 13 over-expressed druggable targets (i.e., EGFR, TF, VEGF) and >10 experimental targets. To rank each drug’s potential success, we measured the co-expression of each target ligand-receptor pair (L/R), reducing the candidates to 4 pathways (TF/TFRC> VEGFA/NRP1> PGF/NRP1> TGFB1/VASN>VEGFA/GPC1). Furthermore, TF/TFRC and VEGFA/NRP1 expected downstream genes were differentially over-expressed where positive LR signal was detected. Similarly, we used ST to define the cellular diversity within a sonic hedgehog (SHH) patient-derived model of Medulloblastoma (MB) and identified how cells specific to a transcriptional state or spatial location are pivotal in responses to treatment with the CDK4/6 inhibitor, Palbociclib. We distinguished neoplastic and non-neoplastic cells within tumours and from the surrounding cerebellar tissue, further refining pathological annotation. We identified a regional response to Palbociclib, with reduced proliferation and induced neuronal differentiation in both treated tumours. Additionally, we resolved at cellular resolution a distinct tumour “interface” where the tumour cells contacted neighbouring mouse brain tissue, consisting of abundant astrocytes and microglia, and continued to proliferate despite Palbociclib treatment. Our data highlight the power of a spatial multi-omics approach to characterise the response of a tumour to targeted therapy and provide further insights into the molecular and cellular basis underlying the response and resistance to cancer therapies. Citation Format: Xiao Tan, Andrew Causer, Tuan Quang Anh Vo, Ning Ma, Bassem Ben Cheikh, Laura Genovesi, Jazmina Gonzalez-Cruz, Quan Nguyen, Oliver Braubach. Applying spatial omics and computational integrative analyses to study drug responses and cancer immune cell interactions. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4703.
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