Background Immune checkpoint therapies (ICTs) targeting the programmed cell death-1 (PD1)/programmed cell death ligand-1 (PD-L1) pathway have improved outcomes for patients with non-small cell lung cancer (NSCLC), particularly those with high PD-L1 expression. However, the predictive value of manual PD-L1 scoring is imperfect and alternative measures are needed. We report an automated image analysis solution to determine the predictive and prognostic values of the product of PD-L1+ cell and CD8+ tumor infiltrating lymphocyte (TIL) densities (CD8xPD-L1 signature) in baseline tumor biopsies. Methods Archival or fresh tumor biopsies were analyzed for PD-L1 and CD8 expression by immunohistochemistry. Samples were collected from 163 patients in Study 1108/NCT01693562, a Phase 1/2 trial to evaluate durvalumab across multiple tumor types, including NSCLC, and a separate cohort of 199 non-ICT- patients. Digital images were automatically scored for PD-L1+ and CD8+ cell densities using customized algorithms applied with Developer XD™ 2.7 software. Results For patients who received durvalumab, median overall survival (OS) was 21.0 months for CD8xPD-L1 signature-positive patients and 7.8 months for signature-negative patients ( p = 0.00002). The CD8xPD-L1 signature provided greater stratification of OS than high densities of CD8+ cells, high densities of PD-L1+ cells, or manually assessed tumor cell PD-L1 expression ≥25%. The CD8xPD-L1 signature did not stratify OS in non-ICT patients, although a high density of CD8+ cells was associated with higher median OS (high: 67 months; low: 39.5 months, p = 0.0009) in this group. Conclusions An automated CD8xPD-L1 signature may help to identify NSCLC patients with improved response to durvalumab therapy. Our data also support the prognostic value of CD8+ TILS in NSCLC patients who do not receive ICT. Trial registration ClinicalTrials.gov identifier: NCT01693562 . Study code: CD-ON-MEDI4736-1108. Interventional study (ongoing but not currently recruiting). Actual study start date: August 29, 2012. Primary completion date: June 23, 2017 (final data collection date for primary outcome measure). Electronic supplementary material The online version of this article (10.1186/s40425-019-0589-x) contains supplementary material, which is available to authorized users.
BackgroundIntratumoural heterogeneity (ITH) is well recognised in prostate cancer (PC), but its role in high-risk disease is uncertain. A prospective, single-arm, translational study using targeted multiregion prostate biopsies was carried out to study genomic and T-cell ITH in clinically high-risk PC aiming to identify drivers and potential therapeutic strategies.Patients and methodsForty-nine men with elevated prostate-specific antigen and multiparametric-magnetic resonance imaging detected PC underwent image-guided multiregion transperineal biopsy. Seventy-nine tumour regions from 25 patients with PC underwent sequencing, analysis of mutations, copy number and neoepitopes combined with tumour infiltrating T-cell subset quantification.ResultsWe demonstrated extensive somatic nucleotide variation and somatic copy number alteration heterogeneity in high-risk PC. Overall, the mutational burden was low (0.93/Megabase), but two patients had hypermutation, with loss of mismatch repair (MMR) proteins, MSH2 and MSH6. Somatic copy number alteration burden was higher in patients with metastatic hormone-naive PC (mHNPC) than in those with high-risk localised PC (hrlPC), independent of Gleason grade. Mutations were rarely ubiquitous and mutational frequencies were similar for mHNPC and hrlPC patients. Enrichment of focal 3q26.2 and 3q21.3, regions containing putative metastasis drivers, was seen in mHNPC patients. We found evidence of parallel evolution with three separate clones containing activating mutations of β-catenin in a single patient. We demonstrated extensive intratumoural and intertumoural T-cell heterogeneity and high inflammatory infiltrate in the MMR-deficient (MMRD) patients and the patient with parallel evolution of β-catenin. Analysis of all patients with activating Wnt/β-catenin mutations demonstrated a low CD8+/FOXP3+ ratio, a potential surrogate marker of immune evasion.ConclusionsThe PROGENY (PROstate cancer GENomic heterogeneitY) study provides a diagnostic platform suitable for studying tumour ITH. Genetic aberrations in clinically high-risk PC are associated with altered patterns of immune infiltrate in tumours. Activating mutations of Wnt/β-catenin signalling pathway or MMRD could be considered as potential biomarkers for immunomodulation therapies.Clinical Trials.gov IdentifierNCT02022371
BackgroundProstate cancer (PCa) has been under investigation as a target for antigen-specific immunotherapies in metastatic disease settings for the last two decades leading to a licensure of the first therapeutic cancer vaccine, Sipuleucel-T, in 2010. However, neither Sipuleucel-T nor other experimental PCa vaccines that emerged later induce strong T-cell immunity.MethodsIn this first-in-man study, VANCE, we evaluated a novel vaccination platform based on two replication-deficient viruses, chimpanzee adenovirus (ChAd) and MVA (Modified Vaccinia Ankara), targeting the oncofetal self-antigen 5T4 in early stage PCa. Forty patients, either newly diagnosed with early-stage PCa and scheduled for radical prostatectomy or patients with stable disease on an active surveillance protocol, were recruited to the study to assess the vaccine safety and T-cell immunogenicity. Secondary and exploratory endpoints included immune infiltration into the prostate, prostate-specific antigen (PSA) change, and assessment of phenotype and functionality of antigen-specific T cells.ResultsThe vaccine had an excellent safety profile. Vaccination-induced 5T4-specific T-cell responses were measured in blood by ex vivo IFN-γ ELISpot and were detected in the majority of patients with a mean level in responders of 198 spot-forming cells per million peripheral blood mononuclear cells. Flow cytometry analysis demonstrated the presence of both CD8+ and CD4+ polyfunctional 5T4-specific T cells in the circulation. 5T4-reactive tumor-infiltrating lymphocytes were isolated from post-treatment prostate tissue. Some of the patients had a transient PSA rise 2–8 weeks following vaccination, possibly indicating an inflammatory response in the target organ.ConclusionsAn excellent safety profile and T-cell responses elicited in the circulation and also detected in the prostate gland support the evaluation of the ChAdOx1-MVA 5T4 vaccine in efficacy trials. It remains to be seen if this vaccination strategy generates immune responses of sufficient magnitude to mediate clinical efficacy and whether it can be effective in late-stage PCa settings, as a monotherapy in advanced disease or as part of multi-modality PCa therapy. To address these questions, the phase I/II trial, ADVANCE, is currently recruiting patients with intermediate-risk PCa, and patients with advanced metastatic castration-resistant PCa, to receive this vaccine in combination with nivolumab.Trial registrationThe trial was registered with the U.S. National Institutes of Health (NIH) Clinical Trials Registry (ClinicalTrials.gov identifier NCT02390063).
Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6–7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.
Background T-DXd (Enhertu®) is an FDA-approved antibody-drug conjugate (ADC) targeting HER2. T-DXd has shown anti-tumor activity, not only in patients with HER2-overexpressing (IHC3+/2+ ISH+) breast cancer (BC) but also in patients with BC with low HER2 expression (IHC1+/2+ ISH−). Current HER2 protein expression assessment is based on manual pathologist scoring that classifies tumors by the percentage of tumor cells with highest intensity and completeness of staining. A critical need exists for more objective and quantitative methods to assess HER2 expression, specifically to better identify patients with low-level expression if T-DXd proves to be efficacious in this patient population. Methods We used deep learning (DL)-based image analysis (IA) to generate a novel HER2 Quantitative Continuous Score (QCS). Data analytic techniques determined optimal HER2 QCS for the J101 trial (NCT02564900) of 151 patients with varying HER2 expression levels (1+, 2+, 3+). HER2 QCS consists of DL models to detect membrane, cytoplasm, and nuclei of all tumor cells. QCS was extensively trained using pathologists’ annotations, and the performance was validated on unseen data to ensure its generalization and robustness. QCS was blindly applied to J101 data. The optical density (OD; level of brown stain intensity) was computed on detected membrane to derive features that could be linked to survival prediction. QCS features were selected to maximize ORR in positive group, minimize ORR in negative group maintaining while high prevalence in the positive group. Results Analytical validation showed high correlation between QCS from automatically detected membranes and QCS from those annotated by pathologists (R=0.993). This is in the same range as correlation between three pathologists (R=0.995). HER2 QCS was largely consistent with pathologist HER2 scoring as well but showed broad quantitative overlap between IHC and ISH categories. HER2 QCS showed a direct linear relationship between ORR and increased HER2 expression across the entire assay range. In the HER2-low population (n = 65), for whom HER2-targeting therapies are not currently approved, 42% of patients responded to T-DXd, with a median PFS (mPFS) of 11 mo. Using HER2 QCS, we were able to further stratify this population into a subgroup of QCS-high patients (above a staining intensity cut-off determined by IA), with response and mPFS increased to 53% (95% CI: 36%-68%) and 14.5 mo (95% CI: 10.9 mo-NR) respectively, while the QCS-low group only showed ORR of 24% (95% CI: 9%-45%) and mPFS of 8.6 mo (95% CI: 4.2 mo-NR). Generally, best-performing QCS cutoffs were driven by most tumor cells expressing a minimal amount of HER2, in contrast to current clinical guidelines that are driven by a minority of cells expressing higher levels of HER2. We also examined spatial heterogeneity by characterizing cells as either bearing membrane stain above a determined OD threshold (positive cell) or lying within certain distances from a positive cell. We observed similar efficacy with best performing-cutoffs, again, being found when a minimal level of HER2 expression (OD) was examined. Conclusions Taken together, these data establish a first proof-of-concept demonstrating that use of HER2 QCS can potentially enhance prediction of patient outcome with T-DXd by increasing sensitivity and specificity of response, especially in the HER2-low population. The ability to identify patients in the HER2-low group who could benefit from T-DXd is critical for its use in a patient population with a high unmet need that would otherwise not be treated with anti-HER2 therapy. Further clinical verification and validation is ongoing. Citation Format: Mark Gustavson, Susanne Haneder, Andreas Spitzmueller, Ansh Kapil, Katrin Schneider, Fabiola Cecchi, Sriram Sridhar, Guenter Schmidt, Sotirios Lakis, Regina Teichert, Anatoliy Shumilov, Ana Hidalgo-Sastre, Magdalena Wienken, Hadassah Sade, J. Carl Barrett, Danielle Carroll. Novel approach to HER2 quantification: Digital pathology coupled with AI-based image and data analysis delivers objective and quantitative HER2 expression analysis for enrichment of responders to trastuzumab deruxtecan (T-DXd; DS-8201), specifically in HER2-low patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-01.
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