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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known to present with pulmonary and extra-pulmonary organ complications. In comparison with the 2009 pandemic (pH1N1), SARS-CoV-2 infection is likely to lead to more severe disease, with multi-organ effects, including cardiovascular disease. SARS-CoV-2 has been associated with acute and long-term cardiovascular disease, but the molecular changes govern this remain unknown.In this study, we investigated the landscape of cardiac tissues collected at rapid autopsy from SARS-CoV-2, pH1N1, and control patients using targeted spatial transcriptomics approaches. Although SARS-CoV-2 was not detected in cardiac tissue, host transcriptomics showed upregulation of genes associated with DNA damage and repair, heat shock, and M1-like macrophage infiltration in the cardiac tissues of COVID-19 patients. The DNA damage present in the SARS-CoV-2 patient samples, were further confirmed by γ−H2Ax immunohistochemistry. In comparison, pH1N1 showed upregulation of Interferon-stimulated genes (ISGs), in particular interferon and complement pathways, when compared with COVID-19 patients.These data demonstrate the emergence of distinct transcriptomic profiles in cardiac tissues of SARS-CoV-2 and pH1N1 influenza infection supporting the need for a greater understanding of the effects on extra-pulmonary organs, including the cardiovascular system of COVID-19 patients, to delineate the immunopathobiology of SARS-CoV-2 infection, and long term impact on health.

In recent years, pregnant people infected with the SARS-CoV-2 virus have shown a higher incidence of “preeclampsia-like syndrome”. Preeclampsia is a systematic syndrome that affects 5-8 % of pregnant people worldwide and is the leading cause of maternal mortality and morbidity. It is unclear what causes preeclampsia, and is characterised by placental dysfunction, leading to poor placental perfusion, maternal hypertension, proteinuria, thrombocytopenia, or neurological disturbances.In this study, we used whole-transcriptome, digital spatial profiling of placental tissues to analyse the expression of genes at the cellular level between placentae from pregnant participants who contracted SARS-CoV-2 in the third trimester of their pregnancy and those prior to the start of the pandemic. Our focused analysis of the trophoblast and villous core stromal cell populations revealed tissue-specific pathways enriched in the SARS-CoV-2 placentae that align with a pre-eclampsia signature. Most notably, we found enrichment of pathways involved in vascular tension, blood pressure, inflammation, and oxidative stress.This study illustrates how spatially resolved transcriptomic analysis of placental tissue can aid in understanding the underlying pathogenic mechanisms of SARS-CoV-2 in pregnancy that are thought to induce “preeclampsia-like syndrome”. Moreover, our study highlights the benefits of using digital spatial profiling to map the crosstalk between trophoblast and villous core stromal cells linked to pathways involved in “preeclampsia-like syndrome” presenting in pregnant people with SARS-CoV-2.

Objectives: Attaining tumour material from lung cancer patients can be challenging with limited sample availability. Therefore, non-invasive means of assessing tumour material is becoming increasingly more important. Circulating tumour DNA (ctDNA), extracted from a blood sample is appealing for the patient, and can be performed serially over the course of treatment. Materials and Methods: Here, we describe an approach for profiling the blood samples of 103 NSCLC patients for 73 variants in ctDNA across a panel of actionable lung cancer mutations using the UltraSEEK lung Panel (Agena Biosciences). Results: Our cross-sectional study showed tumour and blood concordance in the detection of KRAS mutations (G12C, G12D, G12A/V, G12R, G12RC, Q61H) in 17/27 (63%), EGFR mutations (e746_a750del, e747_A750, T790M, L861Q) in 16/20 (80%) with additional PIK3CA_p545K mutations across both cohorts. In patients without reported tumour mutations, 11/56 (19.6%) presented with plasma mutations across EGFR, KRAS and PIK3CA. Where ctDNA mutations were measured longitudinally (n = 4 patients), the individual mutations mirrored the response to therapy/progression of disease. Conclusion: Whilst preliminary, this study demonstrates the utility of detecting clinically actionable mutations in the blood samples of NSCLC patients at the time of presentation, and over the course of therapy.

Background Lung cancers are the leading cause of cancer-related deaths with a 5-year-survival of only ~20%. Whilst immunotherapies have led to durable and prolonged survival, only a subset of patients remain responsive. Additional biomarkers are thus needed to better predict if patients will respond or develop resistance against immune checkpoint inhibitor (ICI) therapies. Spatial phenotyping of the tumor microenvironment (TME) is now recognized as a proxy for ICI therapy outcomes. Our study employs an end-to-end spatial biology strategy to survey non-small-cell lung cancer (NSCLC) tissues for new biomarkers that could guide immunotherapy treatments. Methods We phenotyped pre-treatment biopsies from non-small-cell lung cancer (NSCLC) patients treated with single-agent Nivolumab. We first performed 57-plex whole-slide Single Cell Spatial Phenotyping on the PhenoCycler®-Fusion platform. Next, we developed the spatial analysis of a larger NSCLC cohort using customizable PhenoCode Signature Panels (PSP) for high-throughput immune profile (CD3/CD8/CD20/CD68/PanCK + CD4 add-in) and immuno-contexture (CD8/CD68/PD-L1/FoxP3/PanCK + PD-1 add-in) imaging. The PSP panel content combines the barcode-based antibody chemistry from the PhenoCycler platform with the signal amplification of Opal chemistry from the PhenoImager platform. PSP panels were profiled across n=27 NSCLC biopsies and provided a wider breadth of tissue profiling. Results Our whole-slide single-cell spatial phenotyping analyses revealed high phenotypic diversity in the TME of patients responsive and resistant to ICI therapy. However, higher-throughput analyses of the same tissues using targeted PSP panels revealed no significant differences in quantities of immune cell lineages, including T-cells and macrophages. On the contrary, we discovered multiple quantifiable and statistically significant spatial signatures that appear to be predictive of treatment benefit, which confirms the potential biomarker value of spatial associations in patient tissues. Conclusions This study amounts to a uniquely comprehensive Single Cell Spatial Phenotyping analysis of pre-treatment NSCLC biopsies from a single-agent Nivolumab study. Our data catalogue the diversity in the immune microenvironment of NSCLC but highlighted that immune cell quantification is insufficient to stratify patient cohorts. Single-cell spatial phenotyping, on the other hand, promises to reveal new biomarkers that may aid in better stratification of patients in pre-treatment evaluations. Citation Format: Ning Ma, Aditya Pratapa, James Monkman, Ken O’Byrne, Oliver Braubach, Arutha Kulasinghe. The potential predictive role of spatial phenotyping in non-small cell lung cancer [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 6768.

Background: Immune checkpoint inhibitors (ICI) have proven to be game-changing treatments for mucosal head and neck squamous cell cancer (HNSCC). Emerging successes with anti-PD-1/PD-L1 therapy have led to durable responses and prolonged survival in both human papillomavirus-positive (HPV+) and negative (HPV-) patients. There is now a need for predictive biomarkers to guide patient selection for highly targeted ICI therapies as currently available diagnostic biomarkers have a limited value. The tumor microenvironment (TME) composition, contexture, and cellular architecture are now recognized as key to understanding immune responsive and resistant phenotypes. Here, we are using a spatial biology approach to explore the TME in metastatic/recurrent HNSCC tumors treated with Pembrolizumab/Nivolumab. Methods: In this study, we used single cell, multiomic spatial phenotyping utilizing the PhenoCycler-Fusion spatial biology platform to characterize the TME of HNSCC tumors from a cohort of n=40 patients. The discovery cohort consisted of patients who had complete vs. partial vs. stable vs. progressive responses to ICI therapy. We first analyzed tissues using an ultrahigh-plex antibody panel of >60 antibodies that label immune cell lineages, checkpoints, activation markers as well as tissue structure and the stroma. We then conducted whole-slide, single cell resolution RNA detection with complementary markers on serial sections from the same tissue blocks; the combination of these data allowed us to obtain multiomic spatial signatures that offered uniquely comprehensive insight into the TME of our tissue cohorts. Results: Our study identified highly resolved tissue immune contexture analysis and metabolic tissue signatures associated with resistance and sensitivity to immunotherapy. Most notably, multiomic profiling of HNSCC tumours provided deeper insights into ICI therapy resistance than the single omics based approaches alone. Conclusions: Our study demonstrates the power of unbiased, multiomic spatial phenotyping with whole-slide imaging to identify biomarkers associated with response to ICI therapy in HNSCC. Citation Format: HaYeun Ji, Niyati Jhaveri, Ning Ma, Bassem B. Cheikh, Aditya Pratapa, James Monkman, Ken O’Byrne, Brett Hughes, Arutha Kulasinghe, Oliver Braubach. Single cell, multiomic spatial phenotyping of immunotherapy responses in head and neck cancer. [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 5647.

Background: Cutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer and represents a major global health burden. Approximately 50% of cSCC patients develop primary resistance and 20% will develop secondary resistance to immune checkpoint inhibitors (ICI). There are limited biomarkers that reflect the tumor dynamics predictive of response to ICI therapy, and therefore we need to explore new biomarkers that can better understand the cSCC tumor microenvironment (TME). Methods: Our retrospective study profiled pre-treatment cutaneous skin cancer tissues from patients with immunotherapy sensitive and resistant diseases. In this exploratory study, we designed a high-dimensional > 50-plex antibody panel identifying cell lineages, activation states, and immune checkpoints. In addition, we deployed a combination of antibodies for a novel multiplexed metabolic readout, to enhance our in situ profiling beyond well-known TME targets. Whole-slide spatial phenotyping was conducted on the PhenoCycler-Fusion spatial biology platform. Highplex image analysis was performed using Phenoplex (Visiopharm), including tissue segmentation and cellular phenotyping. Here, we used a deep learning approach to discriminate tumor regions and margins based on their morphology. Spatial analyses including phenotypic composition, cell-to-cell localization, and cellular neighborhoods were performed and compared to clinicopathological findings and responses to ICI therapy. Results: We have profiled the TME of cSCC via ultrahigh-plex, single-cell spatial analyses of whole tissues. Our data have revealed unique cell phenotype compositions and metabolic states within the TME of different patient cohorts. Areas of metabolic activity were visualized and compartmentalized to determine spatial distributions of inherent sensitivity and resistance to ICI therapy. Additionally, a comparison of the phenotypic expression patterns between immunotherapy sensitive and resistant cohorts in the tumor, stromal and margin regions showed relevant differences in the immune content. Taken together, this high-dimensional imaging approach of the cSCC TME has revealed new tissue insights. Conclusion: There is a need to understand the contexture of the cSCC immune microenvironment. Here we identify distinct cellular phenotypic compositions and metabolic states in tissues from immune-competent and immunocompromised cSCC. We thereby confirm biological differences in tissue composition and cellular interactions between these tissues and provide putative new biomarkers for cSCC patient stratification. Citation Format: Niyati Jhaveri, Dmytro Klymyshyn, Bassem B. Cheikh, James Monkman, Dan Winkowski, James Mansfield, Subham Basu, Michael Prater, Nadine Nelson, Gabrielle Belz, Oliver Braubach, Arutha Kulasinghe. The development of a spatial metabolic map of immunotherapy sensitive and resistant cutaneous skin carcinoma. [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 6774.

Immune checkpoint inhibitors (ICIs) Pembrolizumab and Nivolumab have shown promising results in patients with recurrent or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC). Nonetheless, only up to 20% of patients may benefit from them. Next generation predictive biomarkers of response to ICI therapies are currently needed. This retrospective study profiled pre-treatment tissue samples collected from R/M HNSCC patients (n=30) treated with Pembrolizumab or Nivolumab. Targeted spatial proteomics profiling was performed using NanoString’s GeoMx® Digital Spatial Profiler (GeoMx). To develop an informed region-of-interest (ROI) selection strategy for the GeoMx, we analyzed H&E images from serial sections and performed Oncotopix® Discovery software, which considered pathology inputs and machine learning to analyze H&E imagery and 4-channel GeoMx visualization inputs. We profiled 80 proteins simultaneously using the GeoMx across modules in cell death, immune activation, immune cell typing, immuno-oncology drug target, immune profiling, PI3K/AKT signaling, and pan-tumor markers.. Next, we performed differential expression (DE) analysis of the proteins localized in the tumor and stromal compartments against response to therapy parameters of complete, partial, stable and progressive disease (according to RECIST criteria). Four-channel GeoMx immunofluorescence images provided per-ROI cell counts (total and phenotyped based on 3 markers), and these data were used to aid in area and cell count normalization within the tumor and stromal compartments. We found that patients responsive to immunotherapy had higher expression levels of PD-L1, Bcl-2, BCLX, and BIM in the tumor, whereas VISTA, FOXP3, and CD66b were downregulated. In the stromal compartment, it was found that responders had higher expression levels of B7-H3, CD40, and SMA, but lower expression of PARP, S100B, and NY-ESO-1 in comparison to non-responders. In terms of best response analysis, patients with PR (n=5) had higher levels of PD-L1, ER-alpha, and CD68 expression, but lower levels of VISTA, CD27, and CD95/Fas in tumor regions than patients with PD (n=8). In the stroma, PD-L1, CD68, and HLA-DR were upregulated, while VISTA, BIM, and BAD were downregulated in patients with PR versus those with PD. We found that informed ROI selection strategy aided in defining key features in the tumor microenvironment for comparative analysis across samples, and for data normalization methods. Moreover, we found that immune checkpoints and proteins involved in cell death signaling play important roles in the immune responsive tumor microenvironment of HNSCC based on response to immunotherapy. Citation Format: Habib Sadeghirad, Ning Liu, James Monkman, Chin Wee Tan, Caroline Cooper, Jeni Caldera, Sarah Church, Fabian Schneider, James Robert Mansfield, Ken O'Byrne, Melissa Davis, Brett Hughes, Arutha Kulasinghe. Spatial profiling of the tumor microenvironment in head and neck cancer identifies immune checkpoints and proteins involved in cell death signaling biomarkers of immunotherapy response. [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 4626.

BackgroundRobust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness.MethodsWe conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients.FindingsWe show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression is associated with the presence of a high viral load. We further demonstrate that systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 severity. For clinical outcome prediction (e.g. respiratory failure), IFI27 expression displays a high positive (0.83) and negative (0.95) predictive value, outperforming all other known predictors of COVID-19 severity. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 swine influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients.InterpretationThese data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus.Research in contextEvidence before this studyWe searched the scientific literature using PubMed to identify studies that used the IFI27 biomarker to predict outcomes in COVID-19 patients. We used the search terms “IFI27”, “COVID-19, “gene expression” and “outcome prediction”. We did not identify any study that investigated the role of IFI27 biomarker in outcome prediction. Although ten studies were identified using the general terms of “gene expression” and “COVID-19”, IFI27 was only mentioned in passing as one of the identified genes. All these studies addressed the broader question of the host response to COVID-19; none focused solely on using IFI27 to improve the risk stratification of infected patients in a pandemic.Added value of this studyHere, we present the findings of a multi-cohort study of the IFI27 biomarker in COVID-19 patients. Our findings show that the host response, as reflected by blood IFI27 gene expression, accurately predicts COVID-19 disease progression (positive and negative predictive values; 0.83 and 0.95, respectively), outperforming age, comorbidity, C-reactive protein and all other known risk factors. The strong association of IFI27 with disease severity occurs not only in SARS-CoV-2 infection, but also in other respiratory viruses with pandemic potential, such as the influenza virus. These findings suggest that host response biomarkers, such as IFI27, could help identify high-risk COVID-19 patients - those who are more likely to develop infection complications - and therefore may help improve patient triage in a pandemic.Implications of all the available evidenceThis is the first systemic study of the clinical role of IFI27 in the current COVID-19 pandemic and its possible future application in other respiratory virus pandemics. The findings not only could help improve the current management of COVID-19 patients but may also improve future pandemic preparedness.

The era of precision medicine has been powered in part by advancements in the field of immuno-oncology, which postulates that harnessing the body’s own immune system is key to eliminating cancer. Immunotherapies have, to date, revolutionised the field of oncology. The immune contexture of the tumour microenvironment (TME) is an important factor in dictating how well a tumour may respond to immune checkpoint inhibitors (ICI), but biomarker information alone is insufficient to design appropriate treatment strategies. Instead, we now understand that spatial classification of the immune contexture within the TME is foundational towards addressing how immunological composition and status dictates responses to therapy. In this project, we used a combination of high throughput and high-fidelity spatial biomarker detection strategies to thoroughly characterize the TME of non-small cell lung carcinoma tissue samples against outcomes for immunotherapy arm of the trial (Nivolumab) and standard of care (chemotherapy). Experimental cohorts were categorized into four pools, consisting of tissues from complete responders, partial responders, stable disease and progressive disease patients. We first analyzed tissues via whole-slide multiplex-immunohistochemistry (mIHC) using the Vectra Polaris spatial imaging platform. For this purpose we used 3 x 6 plex antibody panels, aimed at T-cell profiling, immune exhaustion and immuno-modulatory targets. We then expanded upon this work using an ultra-high plex antibody imaging panel of 50 or more antibodies with the aim at identifying additional spatial signatures in the different experimental conditions. The latter experiments were conducted with CODEX system, which readily provides whole-slide, single cell resolution imaging data from FFPE samples. Both datasets were for distinct spatial profiles in each experimental condition and compared to their clinical outcome to therapy via discriminant analyses. Identifying the benefit of treating patients with immune checkpoint inhibitor therapy prior to treatment will ensure better outcomes given that immunotherapies can only fight tumours that the immune system ‘sees’ and recognises as foreign and harmful. However, in tumour tissue, these biomarkers are highly variable and change dynamically. Therefore, there is growing consensus for the development of additional and more predictive biomarkers of response to ICI therapy. Spatial phenotyping of the immune landscape will reveal cell types and spatial signatures associated with outcomes to immunotherapy. Citation Format: Arutha Kulasinghe, Ning Ma, James Monkman, Najiba Mammadova, Oliver Braubach, Ken O'Byrne. Profiling spatial signatures in the microenvironment of non-small cell lung carcinomas in immunotherapy responsive and resistant disease cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1714.

Immunotherapies have led to long term benefits in a subset of non-small cell lung cancer (NSCLC) patients. Developing predictive biomarkers of response to therapy is currently an unmet clinical need. The composition of the cellular content of the tumour microenvironment (TME) is an important characteristic driving treatment resistance. In this study, we utilised spatial transcriptomics methods, including Nanostring digital spatial profiling (DSP) and multiplex IHC, to define the tumour/stroma (compartment) specific proteome and transcriptome from a cohort of 2nd line immunotherapy treated NSCLC patients. We identified by mIHC that CD68+ macrophages with PD1+, FoxP3+ cells is significantly enriched in immunotherapy resistant tumours (p=0.012). Moreover, IL2 mRNA (p=0.001) in the stromal compartment was significantly increased in patients that were sensitive to ICI therapy, whilst CTLA-4 and IDO-1 were suppressed in responsive patients. Tumour compartment localised CD44 was depleted in the responder group and corresponded inversely with higher stromal expression of one of its ligands, SPP1 (p=0.008). Orthogonal validation of CD44 by multiplex immunofluorescence confirmed both its association with response and localisation to tumour cells rather than immune cell infiltrate. Cox survival analysis showed that tumour compartment CD44 expression was associated with a poorer prognosis (HR=1.61, p=0.01) whilst stromal localisation of CTLA-4 (HR=1.78, p=0.003) and ARG1 (HR=2.37, p=0.01) were associated with poorer outcome. Through a multi-omics approach, we demonstrate the utility of spatial mapping of NSCLC tumours and provide evidence for the role of a number of compartment-specific biomarkers which may aid in identifying those likely to respond to immunotherapy. Citation Format: Arutha Kulasinghe, James Monkman, Honesty Kim, Aaron Mayer, Ahmed Mehdi, Nicholas Matigian, Marie Cumberbatch, Milan Bhagat, Rahul Ladwa, Scott Mueller, Mark Adams, Ken O'Byrne. Multi-omic dissection of immunotherapy response groups in non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2036.

8544 Background: Immune checkpoint inhibitors (ICI) have shown durable benefit in a subset of non-small cell lung cancer (NSCLC) patients. The composition of the tumour microenvironment (TME) is becomingly increasingly recognised as an important factor to predict response to therapy. Methods: Here, we applied digital spatial profiling of the tumour and stromal compartments from a 2nd line NSCLC ICI-treated cohort (n = 41 patient) and standard of care (SOC), platinum treated NSCLC cohort (n = 47), to identify tissue-based signatures of response to therapy. Results: We demonstrate by mIHC that the interaction of CD68+ macrophages with PD1+, FoxP3+ cells is significantly enriched in ICI refractory tumours (p = 0.012). Patients sensitive to ICI therapy expressed higher levels of IL2 receptor alpha (CD25, p = 0.028) within the tumour compartments, which corresponded with the increased expression of IL2 mRNA (p = 0.001) within their stroma. Immuno-inhibitory markers CTLA-4 (p = 0.021) and IDO-1 (p = 0.023) were supressed in ICI-responsive patients. Tumour CD44 (p = 0.02) was depleted in the response group and corresponded inversely with significantly higher stromal expression of one of its ligands, SPP1 (osteopontin, p = 0.008). Analysis of dysregulated transcripts indicated the potential inhibition of stromal interferon-gamma (IFNγ) activity, estrogen-receptor and Wnt-1 signalling activity within the tumour cells of ICI responsive patients. Cox survival analysis indicated tumour CD44 expression was associated with poorer prognosis (HR = 1.61, p = 0.01), consistent with its depletion in ICI sensitive patients. Similarly, stromal CTLA-4 (HR = 1.78, p = 0.003) and MDSC/M2 macrophage marker ARG1 (HR = 2.37, p = 0.01) were associated with poorer outcome while BAD (HR = 0.5, p = 0.01) appeared protective. The SOC cohort paralleled similar roles for immune checkpoints and pro-apoptotic markers, with LAG3 (HR = 3.81, p = 0.04) indicating poorer outcome, and BIM (HR = 0.16, p = 0.014) with improved outcome. Interestingly, stromal mRNA for E-selectin (HR = 652, p = 0.001), CCL17 (HR = 70, p = 0.006) and MTOR (HR = 1065, p = 0.008) were highly associated with poorer outcome in ICI treated patients, indicating pro-tumourigenic features in the tumour microenvironment that may facilitate ICI resistance. Conclusions: Through multi-modal approaches, we have dissected the characteristics of NSCLC treatment groups and provide evidence for the role of several markers including IL2, CD25, CD44 and SPP1 in the efficacy of current generations of ICI therapy.