Flagship Biosciences Research Articles

Abstract 2530: Macrophage biomarkers CD68 and CD163 correlate with CRC patient survival

Abstract Background: Immune cells within the tumor microenvironment (TME) play a vital role in regulating tumor progression. Therefore, immunotherapies that elicit anti-tumor responses are of great interest for the treatment of various cancers. Macrophages are a current immune cell type of interest due to the ability to polarize into anti-tumor (M1) and pro-tumor (M2) phenotypes. The density and phenotype of macrophages within the tumor and TME have been linked to prognosis in multiple types of solid tumors. Our hypothesis is that colorectal cancer (CRC) patients with high immune infiltration and greater amounts of anti-tumor immune cells within the tumor compartment will have an increased time of survival compared to cancers with immune excluded or desert environments. Methods: One CRC tumor microarray (TMA) containing primary tumors, metastases, and normal tissue were stained via multiplex immunofluorescence (mIF) for 6 different immune markers: CD3, CD8, CD56, CD68, CD163, and PD-L1. The stained TMAs were analyzed utilizing Flagship Biosciences’ proprietary image analysis platform. Machine learning algorithms used cellular features to stratify cells as belonging to either the tumoral or stromal compartment. Core level expression data was pulled and represented on a whole-cohort basis. All staining and image analysis outputs were reviewed by a board-certified, MD pathologist. Kaplan-Meier curves were generated using survival data in relation to the low, medium, and high expression of CD68, CD163, and/or PDL1 in the whole tissue, as well as tumor and stromal compartments. Patients were also stratified into low (1 and 2) and high (3 and 4) cancer stages. Results: There is a correlation between patient survival and the presence or absence of macrophage markers CD68 and CD163 and lack of immune-suppressive marker PDL1. Specifically, lower levels of CD68+CD163- cells within the tumor compartment correlate with an increase in patient survival. Similarly, lower PDL1 expression in the stromal compartment positively correlate with prolonged patient survival in CRC patients. Conclusion: Data generated through Flagship Biosciences’ image analysis platform showed a strong relationship between CD68 and CD163 presence and localization with CRC patient survival. Altering the immune cells within the tumor to an anti-tumor immune environment could increase patient survival times in high stage CRC patient populations. Combining immune checkpoint inhibitors with current FDA approved therapies for CRC are of interest to further extend patient survival, as PDL1 expression negatively correlate with CRC patient survival. Further, utilizing Flagship Biosciences’ image analysis software to understand cancer immune microenvironments should be further utilized to aid in diagnosis and treatment decisions. Citation Format: Dannah Miller, Kate Hieber, Will Paces, Huong Nguyen, Adam Beharry, Roberto Gianani. Macrophage biomarkers CD68 and CD163 correlate with CRC patient survival [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 2530.

Abstract 1720: Macrophage biomarkers CD68 and CD163 correlate with NSCLC patient survival

Abstract Background: Immune cells within the tumor microenvironment (TME) play a vital role in regulating tumor progression. Therefore, immunotherapies that elicit anti-tumor responses are of great interest for the treatment of various cancers. Macrophages are a current immune cell type of interest due to the ability to polarize into anti-tumor (M1) and pro-tumor (M2) phenotypes. The density and phenotype of macrophages within the tumor and TME have been linked to prognosis in multiple types of solid tumors. Our hypothesis is that non-small cell lung carcinoma (NSCLC) patients with high immune infiltration and greater amounts of anti-tumor immune cells within the tumor compartment will have an increased time of survival compared to cancers with immune excluded or immune desert environments. Methods: One NSCLC tumor microarray (TMA) containing primary tumors, metastases, and normal tissue were stained via multiplex immunofluorescence (mIF) for 6 different immune markers: CD3, CD8, CD56, CD68, CD163, and PD-L1. The stained TMAs were analyzed utilizing Flagship Biosciences’ proprietary image analysis platform. Machine learning algorithms used cellular features to stratify cells as belonging to either the tumoral or stromal compartment. Core level expression data was pulled and represented on a whole-cohort basis. All staining and image analysis outputs were reviewed by a board-certified, MD pathologist. Kaplan-Meier curves were generated based on survival data in relation to the low, medium, and high expression of CD68 and/or CD163 in the whole tissue, as well as tumor and stromal compartments. Patients were also stratified into low (1 and 2) and high (3 and 4) cancer stages. Results: There is a correlation between patient survival and the presence or absence of macrophage markers CD68 and CD163. Specifically, lower levels of CD68+CD163- cells within the tumor and stroma compartments correlate with an increase in patient survival. There is also a correlation of a high level of expression of CD163+, CD68+CD163-, or CD68+CD163+ cells with increased patient survival in high stage patients compared to low stage patients. Specifically in high stage NSCLC patients, a higher level of CD163+ expression correlates with a reduced lifespan compared to patients with medium and low expression. Conclusion: Data generated through Flagship Biosciences’ image analysis platform showed a strong relationship between immune cell presence and localization with NSCLC patient survival. Altering the immune cells within the tumor to an anti-tumor immune environment could increase patient survival times. Further, utilizing Flagship Biosciences’ image analysis software to understand cancer immune microenvironments should be further utilized to aid in diagnosis and treatment decisions. Citation Format: Dannah Miller, Kate Hieber, Will Paces, Huong Nguyen, Adam Beharry, Roberto Gianani. Macrophage biomarkers CD68 and CD163 correlate with NSCLC patient survival [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 1720.

Real-world adherence and persistence with anaplastic lymphoma kinase inhibitors in non-small cell lung cancer.

BACKGROUND: Lung cancer is the leading cause of cancer-related deaths in the United States. Several anaplastic lymphoma kinase (ALK) rearrangement inhibitors have been approved for the treatment of metastatic ALK-positive non-small cell lung cancer (NSCLC). Effective disease management requires an understanding of how these treatments are used in clinical practice, since low treatment adherence and/or early discontinuation have been associated with poor patient outcomes. Owing to the recency of approvals, real-world data on the use of ALK inhibitors in patients with ALKpositive NSCLC are currently limited; this represents a notable gap in our understanding of ALK treatment use. OBJECTIVE: To assess real-world adherence and persistence with ALK inhibitors in patients with ALK-positive NSCLC. METHODS: This retrospective observational study used US commercial claims for patients aged at least 18 years with lung cancer receiving ALK inhibitors (alectinib, brigatinib, ceritinib, crizotinib) between July 1, 2015, and December 31, 2018. Patients' first and any subsequent ALK inhibitor uses were categorized into ALK inhibitor-naive and ALK inhibitor-pretreated cohorts, respectively. Adherence was measured by medication possession ratio and persistence by time from treatment initiation to discontinuation (earliest of a treatment switch or greater than a 60-day gap). Descriptive statistics were used to summarize patient characteristics. Cohort comparisons were made using chi-square tests and t-tests. Persistence and time to next ALK inhibitor were analyzed using Kaplan-Meier methods and the log-rank test. Poisson and Cox regression models of adherence and persistence, respectively, were applied to compare ALK inhibitors. RESULTS: We identified 1,482 patients treated with alectinib (n = 445) or crizotinib (n = 1,037) in the ALK inhibitor-naive cohort; 604, 142, and 134 patients received alectinib, brigatinib, or ceritinib in the ALK inhibitor-pretreated cohort. Adherence during the treatment period (95%-97%) and the proportion of patients with a medication possession ratio of at least 0.8 (92%-95%) were similar for all ALK inhibitors. In the ALK inhibitor-naive cohort, median time to treatment discontinuation with alectinib and crizotinib was 27.1 and 8.8 months, respectively; patients receiving alectinib were 46% less likely to discontinue than patients receiving crizotinib (adjusted hazard ratio [aHR] [95% CI]: 0.54 [0.44-0.65]; P < 0.0001). In the ALK inhibitor-pretreated cohort, the discontinuation risk for alectinib was 64% lower than for ceritinib (aHR [95% CI]: 0.36 [0.27-0.49]; P < 0.0001) and 34% lower than for brigatinib (aHR [95% CI]: 0.66 [0.42-1.02]; P = 0.062). CONCLUSIONS: To our knowledge, this study is the first to address a current research gap by assessing real-world adherence and persistence with ALK inhibitors among patients with ALK-positive NSCLC in real-world clinical practice. Alectinib was associated with longer real-world persistence than other ALK inhibitors, despite similar adherence. Further research with more patients and longer follow-up is needed to link persistence to real-world clinical outcomes. DISCLOSURES: This study was funded by Genentech Inc. Ganti has received research support from Takeda and has provided consulting services to Genentech Inc., AstraZeneca, Flagship Biosciences, Cardinal Health, BioGene, Mirati Therapeutics, Blueprint Medicines, and G1 Therapeutics. Lin, Wong, and Ogale are employees of Genentech Inc. and may own stock in F. Hoffmann-La Roche. Yang was employed by Genentech Inc. at the time of this study. Part of the study findings were presented as a poster at the NCCN 2020 Virtual Annual Conference, April 9, 2020.

Thrombosis Prophylaxis with Apixaban in Patients Treated with Asparaginase

Thrombosis Prophylaxis with Apixaban in Patients Treated with Asparaginase

58 Analytical validation of a novel immunohistochemistry assay to determine nuclear AHR expression in human bladder cancer

BackgroundAryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that translocates to the nucleus upon ligand binding, and activates a target gene expression program, contributing to the immunosuppressive state of the tumor microenvironment.1 IK-175 is a selective, oral, potent AHR antagonist that has demonstrated strong immune modulation, and robust tumor growth inhibition, either as a single agent or in combination with anti-PD-1, in mouse models. IK-175 is being evaluated in an ongoing phase 1 clinical study as a single agent and in combination with nivolumab for bladder cancer patients (NCT04200963). We hypothesized that nuclear AHR protein expression in tumors is an indicator of activated AHR signaling, and thus a potential predictive biomarker for the clinical response to IK-175. To test this hypothesis, we have developed a novel AHR immunohistochemistry (IHC) assay and analytically validated it in a Clinical Laboratory Improvement Amendments (CLIA) certified lab. We have successfully implemented it in our ongoing phase 1b clinical trial of IK-175 for prospective patient enrollment.MethodsThe AHR IHC assays were developed in collaboration with Flagship Biosciences and Neogenomics on a Leica Bond RX and a CDx-compatible Leica Bond III platform, respectively. AHR expression pre-characterized cell lines, xenografts, and human bladder cancer samples were used to optimize the IHC conditions for specific and sensitive detection of nuclear AHR protein. The Neogenomics assay was further analytically validated for accuracy, sensitivity, specificity, and precision. The IHC assay developed in Flagship was used as an orthogonal comparator for accuracy testing. A cutoff value for AHR nuclear positivity was also determined by assessing n=200 human bladder cancer specimens.ResultsTo determine the prevalence of nuclear AHR expression in human bladder cancer, two hundred unique human bladder cancer specimens, including sixty bladder cancer blocks and 2 tumor microarrays, were analyzed. A cutoff of 65% tumor cells positive for 2+/3+ nuclear AHR was determined to enable a targeted enrollment of 1 in 3 to 5 patients screened. Assay accuracy, sensitivity, specificity, and precision were assessed, and reached 96%, 100%, 95%, and 100% respectively. The intra-pathologist concordance was also assessed to be 90% concordant. The assay passed the pre-defined acceptance criteria (&gt;85%) in all parameters.ConclusionsA novel and robust nuclear AHR IHC assay for bladder cancer was developed and analytically validated in a CLIA certified lab and showed ≥95% accuracy, specificity, sensitivity, and precision, enabling its implementation in an IK-175 Ph1b clinical study (NCT04200963) for prospective patient enrichment.Trial RegistrationNCT04200963ReferenceCampesato L, Budhu S, Tchaicha J, et al. Blockade of the AHR restricts a Treg-macrophage suppressive axis induced by L-Kynurenine. Nature Communications 2020; 11:4011

Abstract 433: Clinical validation of a Dickkopf-1 (DKK1) chromogenic in-situ hybridization (CISH) assay for manual and image-analysis assisted pathologist interpretation

Abstract Dickkopf-1 (DKK1) is a secreted modulator of Wnt signaling that is frequently overexpressed in tumors and associated with a poor prognosis. In this study we demonstrate an approach for clinically validating a RNAscope chromogenic in situ hybridization (CISH) assay for determining the level of DKK1 RNA in Gastric (G) and Gastroesophageal (GEJ) tumor tissues according to CLIA guidelines. This two-step process validated first the performance of the wet assay along with the ability of a pathologist to manually score the CISH signal according to a dot-based H-score paradigm, and second the ability of image analysis (IA) software (Flagship Biosciences) to unbiasedly and reproducibly quantify DKK1 staining in the same set of samples. The DKK1 CISH assay for manual scoring passed all pre-determined criteria of sensitivity, specificity, accuracy, and precision. 100% of the 40 evaluated G/GEJ tissues demonstrated acceptable staining in target tumor cells and absence in non-target cells. 100% of the evaluated tissues passed sensitivity with a broad dynamic range of signal expression across target cells, and negligible background staining. Reproducibility was measured by blinded pathology scoring of a serial subset of 12 cases, resulting in 11/12 (92%) with concordant DKK1 H-scores. Accuracy was assessed with a DKK1 qPCR assay on a 20-sample subset and a significant correlation with the H-score data was observed. The IA algorithm also passed all pre-determined criteria of sensitivity, specificity, accuracy, and precision. 36/40 samples (90%) passed analytical specificity with the IA algorithm correctly classifying staining on true cells belonging to the tumor. Failed samples were attributed to non-specific alkaline phosphatase activity, which in practice would be disregarded by the reviewing pathologist. 100% of samples passed the sensitivity criteria of appropriate cell identification and classification. IA precision compared H-scores across 3 days of staining, with 11/12 (92%) cases having concordant DKK1 H-scores and an ICC of 0.9009 (95% CI: 0.7117-0.9601). Digital H-scores were highly correlated to the validated manual H-scores of the 40-sample set (r = 0.72, p &amp;lt;0.0001). Taken together, these data demonstrate a clinically validated DKK1 RNAscope CISH laboratory-derived test (LDT) for manual and IA-assisted pathologist interpretation. The DKK1 RNAscope LDT is currently being applied as part of a phase 2 clinical study of DKN-01 in combination with tislelizumab to prospectively identify previously treated G/GEJ adenocarcinoma patients with elevated DKK1 tumor expression (NCT04363801). Citation Format: Charles Caldwell, Mike Kagey, Sofia Reitsma, Will Paces, Elizabeth Bueche, Vitria Adisetiyo, Roberto Gianani. Clinical validation of a Dickkopf-1 (DKK1) chromogenic in-situ hybridization (CISH) assay for manual and image-analysis assisted pathologist interpretation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 433.

Abstract 4923: Analysis of companion diagnostic potentials for multifaceted PD-L1 assays

Abstract In vitro diagnostic (IVD) approvals of qualitative immunohistochemical (IHC) assays offer unique patient selection strategies by equipping pathologists with new tools to assess tumor status including tumor immune landscape. PD-L1/PD-1 checkpoint therapies require accurate diagnostic tools for optimal patient selections, however the interpretation of these tests may be very complicated upon manual assessment. As assessment criteria have increased in complexity, an accurate quantitative approach is needed to objectively and consistently interpret challenging paradigms. Recent IVD tests have multifaceted paradigms which assess both tumor and immune positivity. Although the Ventana PD-L1 SP263 and the Dako PD-L1 22C3 qualitative diagnostics are guiding immunotherapy decisions, interpretation of the results may be subjective and challenging in many cases. This creates a further level of complexity as tumor cell positivity, and immune cell presence and positivity must be assessed in combination. In this study, we have used Flagship Biosciences’ image analysis platform (cTA®) to explore the use of artificial intelligence (AI) and machine learning in the context of complex PD-L1 assays to provide accurate and precise quantification that allows for objective interpretation of the IVD. Non-small cell lung cancer (NSCLC) and urothelial carcinoma (UC) samples were stained with the SP263 and 22C3 PD-L1 assays and image analysis was used to provide an interpretation status based upon the IVD scoring paradigms. A comparison of the cTA results across both tests and tissue indications was also performed to explore how we may further support cross-platform PD-L1 solutions that provide adaptable and objective quantification. The use of Flagship’s cTA platform to identify per-cell biofeatures, using machine learning, may successfully quantify PD-L1 staining in various cell populations. The ability to independently assess these populations allows for a consistent and unbiased method for the assessment tumor status for PD-L1 immunotherapy treatment decisions. Citation Format: Jeni Caldara, Joseph S. Krueger, Elliott Ergon, Staci Kearney, Bharathi Vennapusa. Analysis of companion diagnostic potentials for multifaceted PD-L1 assays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4923.

Abstract 4035: Protein tyrosine kinase 7 (PTK7) biomarker analysis in patients (pts) treated with PF-06647020, a PTK7 antibody-drug conjugate (ADC), in a phase I dose expansion study

Abstract Background: PF-06647020, an ADC comprising a humanized monoclonal antibody against PTK7, a cleavable valine-citrulline linker, and an auristatin payload, is being investigated in an ongoing Phase I clinical trial in pts with advanced solid tumors. We hypothesized that response to a PTK7-directed ADC would correlate with PTK7 expression in the pts tumor. We report preliminary baseline PTK7 expression in pts tumors and clinical response using a novel CLIA validated laboratory developed test (LDT). Methods: We developed and validated an LDT comprising an anti-PTK7 immunohistochemistry assay with digital tissue analysis using Flagship Biosciences Inc’s cTA® platform to detect membrane-associated PTK7 in formalin-fixed paraffin embedded (FFPE) epithelial malignancies of ovary, breast and lung. Tumor samples were immunolabeled with the LDT and image analysis was applied to derive a digital H-score. Specifically the digital image annotation included desired regions of analysis (ROA) and excluded regions not of interest. The cTA® platform identified each cell in a given ROA and quantified an overall H-score for malignant epithelial cell membranes immunolabeled for PTK7. Non-small cell lung cancer (NSCLC) pts with H-scores ≥ 56.8 were eligible for enrollment whereas ovarian cancer (OVCA) pts were enrolled unselected. Triple negative breast cancer (TNBC) pts were initially enrolled unselected, but after review a requirement for H-scores ≥ 92.8 was implemented for enrollment. Results: PTK7 IHC was performed on baseline FFPE samples from 69 pts and digital H-scores were generated (Table 1). An additional 7 pt samples were not amenable to analysis with the cTA® platform. Conclusions: A novel CLIA validated LDT was developed and implemented to assess PTK7 baseline levels in FFPE tumors from pts treated with PF-06647020. Clinical responses tended to correlate with H-scores that were higher than the mean for each tumor type. Table 1.PTK7 digital H-scores in Phase I dose expansionNumber of SamplesMinimum H-scoreMaximum H-scoreMean H-scoreMean H-score of Non-respondersMean H-score of RespondersOVCA31120089.280.2111.2NSCLC1993287167.8159.2200.3TNBC1953258159.2147.0224.3 Citation Format: Amy Jackson-Fisher, Navi Mehra, Roberto Gianani, Pamela Whalen, Pamela Vizcarra, Shibing Deng, Stephen McComish, Xiaohua Xin, Eric L. Powell. Protein tyrosine kinase 7 (PTK7) biomarker analysis in patients (pts) treated with PF-06647020, a PTK7 antibody-drug conjugate (ADC), in a phase I dose expansion study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4035.

Abstract 4893: Combined immunohistochemistry and NGS-based patient profiling for predicting anti-PD-1/PD-L1 therapy response

Abstract Introduction: There are several different modalities of predictive tests which support response to anti-PD-1/PD-L1 inhibitors therapy, including PD-L1 expression by immunohistochemistry (PD-L1 IHC), mismatch repair deficiency (dMMR), microsatellite instability (MSI), and recently emerging tumor mutation burden (TMB), and Gene Expression Panels (GEP). Each of these methods capture different facets of the immune system: TMB and MSI evaluates represents mutational/neoantigen load which can stimulate the immune system; GEP establishes a profile of immune response, and whereas PD-L1 IHC directly evaluates the state of checkpoint inhibition in the tumor and tumor microenvironment (TME). We constructed a compound testing paradigm for immune system monitoring called PredicineX, which combines genome analysis which relies on tissue or blood-derived nucleic acids and advanced tissue context analytics based on PD-L1 IHC in solid tissue biopsies to create a comprehensive patient profile to support anti-PD-1/PD-L1 therapy decision making. Methods: Using Predicine’s GeneRADAR technology, we developed a tissue- and blood-based NGS assay to capture genomic alterations in cancer genes including tumor mutation burden (TMB) and microsatellite instability (MSI). Using Flagship Biosciences cTA® tissue image analysis technology, we created an artificial intelligence (AI) based PD-L1 IHC scoring platform which provides both current PD-L1 IHC scoring paradigms and novel computational scores from the rich tissue context data profile created from PD-L1 IHC slides using this technology. We compared biomarker profiles in a cohort of patients using the two technologies and created an integrative data profile to evaluate its association with anti-PD-1/PD-L1 therapy response. Results: We demonstrate the synergistic value of combining genomic based TMB, MSI, and GEP immune profiles with contextual information from PD-L1 IHC slides in patient biopsies. The high complexity gene profile, combined with the rich tissue context data, provided a novel means to stratify patients into 4 categories: 1)mutation high/immune competent; 2)mutation low/immune deficient; 3)mutation high/immune deficient; and 4) mutation low/immune competent. Notably, a strong association between the stratified groups and anti-PD-1/PD-L1 therapy was identified. Conclusion: In this study, we have demonstrated the feasibility of a compound test relying on tissue- and blood-based NGS assay and PD-L1 IHC which can ascribe differential phenotypes to patients for clinically relevant and actionable decisions about response to anti-PD-1/PD-L1 therapy. Citation Format: Jianjun Yu, Jianguo Dong, Amy Wang, Joseph S. Krueger. Combined immunohistochemistry and NGS-based patient profiling for predicting anti-PD-1/PD-L1 therapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4893.

Utilization of a Novel Method of Detection of CD30 Expression in Diffuse Large B-Cell Lymphoma

Utilization of a Novel Method of Detection of CD30 Expression in Diffuse Large B-Cell Lymphoma

DMD CLINICAL THERAPIES II: P.132Identification of developmental myosin positive fibres acts both as a clinical biomarker for muscle disease and an important component of the process to confirm ezutromid target engagement

The expression of developmental and neonatal myosin heavy chains can be detected in newly formed regenerating skeletal myofibres 2-3 days after injury and remain for 2-3 weeks. These myosin isoforms are transiently expressed during regeneration and replaced when adult fast and slow myosins become prevalent. Therefore, the presence of developmental myosin heavy chain (MHCd) provides a very specific biomarker of regenerating fibres in pathologic skeletal muscle including DMD. Utrophin is also expressed in regenerating myofibres, following a similar restricted expression profile to MHCd. Summit have utilised novel automated immunohistochemistry imaging algorithms, in partnership with Flagship Biosciences, to quantify expression of developmental myosin heavy chain (MHCd) and utrophin in DMD patient muscle biopsies. This technology was utilised in the current Phase 2 open label study of ezutromid administered to 40 ambulatory patients with DMD where key secondary endpoints are from muscle biopsies; collected at baseline (n=40) and treatment Week 24 (n=25) or Week 48 (n=15). Compared to baseline, the Week 24 biopsies demonstrated a statistically significant decrease in the number of fibres expressing MHCd; the average reduction was -2.61% (95% CI -4.33, -0.90) from mean baseline of 11.37%; a relative reduction of 23%. This suggests a significant decrease in the number of fibres undergoing the natural repair process following damage. Additional functionality of the algorithms developed allows for assessment of expression heterogeneity across fibres and quantification of MHCd expression and utrophin expression in the same fibre. The individual fibre specific data analyses quantifying MHCd and utrophin comparing baseline to both Week 24 biopsies and Week 48 biopsies will be presented including those profiles that potentially distinguish a utrophin profile specific to ezutromid modulation.

Validation of a Muscle-Specific Tissue Image Analysis Tool for Quantitative Assessment of Dystrophin Staining in Frozen Muscle Biopsies.

Duchenne muscular dystrophy is a rare, progressive, and fatal neuromuscular disease caused by dystrophin protein loss. Common investigational treatment approaches aim at increasing dystrophin expression in diseased muscle. Some clinical trials include assessments of novel dystrophin production as a surrogate biomarker of efficacy, which may predict a clinical benefit from treatment. To establish an immunofluorescent scanning and digital image analysis workflow that provides an objective approach for staining intensity assessment of the immunofluorescence dystrophin labeling and determination of the percentage of biomarker-positive fibers in muscle cryosections. Optimal and repeatable digital image capture was achieved by a rigorously qualified fluorescent scanning process. After scanning qualification, the MuscleMap (Flagship Biosciences, Westminster, Colorado) algorithm was validated by comparing high-power microscopic field total and dystrophin-positive fiber counts obtained by trained pathologists to data derived by MuscleMap. Next, the algorithm was tested on whole-slide images of immunofluorescent-labeled muscle sections from Duchenne muscular dystrophy, Becker muscular dystrophy, and control patients. When used under the guidance of a trained pathologist, the digital image analysis tool met predefined validation criteria and demonstrated functional and statistical equivalence with manual assessment. This work is the first, to our knowledge, to qualify and validate immunofluorescent scanning and digital tissue image-analysis workflow, respectively, with the rigor required to support the clinical trial environments. MuscleMap enables analysis of all fibers within an entire muscle biopsy section and provides data on a fiber-by-fiber basis. This will allow future clinical trials to objectively investigate myofibers' dystrophin expression at a greater level of consistency and detail.

Abstract 4582: Evaluating benefits of PD-L1 image analysis for the clinical setting

Abstract Tissue-based investigations can prove challenging due to complex tissue architecture and heterogeneous biomarker expression, visual and cognitive “traps” that affect interpretive precision, and subjective assessments that affect reproducibility. A major concern is that these challenges could increase the risk of failure for therapeutic/diagnostic co-development and clinical use, as the biomarker measurements continue to increase in complexity and require increasingly precise diagnostic cut-points. Image analysis tools have been developed to overcome some of the challenges for conventional anatomic pathology practices, capitalizing on the objectivity and computational power of a digital platform. A computer, however, lacks the cognitive ability and experience of a human to interpret tissue architecture and context. Flagship Biosciences’ computational Tissue Analysis (cTA™) platform integrates the power of our tissue Image Analysis (tIA™) technology with the contextual experience of an anatomic pathologist to produce robust, precise, quantitative results that demonstrate biomarker content in the tissue context. Flagship Biosciences envisions the integration of our cTA™ technology into a computer-aided clinical pathology workflow as a method to improve the precision of scoring for even some of the most challenging tissue-based biomarker measurements. In a proof-of-concept study, we evaluated the performance of manual versus digital scoring approaches in a cohort of non-small cell lung carcinoma (NSCLC) samples stained with the IHC protocol for the PD-L1 PharmDx 28-8 complementary diagnostic. A comparison of the 2 modalities demonstrated that in nearly all cases, the within sample standard deviation of the cTA™ digital score results was less than the manual score (median inter-pathologist %CVs were reduced from 124.9% to 7.8% and intra-pathologists from 65.4% to 7.6% for manual and digital scores, respectively). As an additional exploratory examination, the effect of heterogeneity on PD-L1 interpretation was also investigated. Pathologists evaluated the same whole tissue slides within 5 high powered fields (HPFs) using both manual and cTA™ -derived scoring. Results demonstrated that the use of cTA™ provides improves agreement between HPF and whole slide assessments (absolute difference between the manual scores from HPF to whole slide were larger than the absolute differences for the digitally derived scores, at 3.14% and 8.27%, respectively). Taken together, these studies demonstrate that the use of cTA™ can significantly reduce variability in PD-L1 scoring, as compared to a manual scoring approach. Citation Format: Staci Kearney, Joshua Black, Famke Aeffner, Joshua Black, Luke Pratte, Joseph Krueger. Evaluating benefits of PD-L1 image analysis for the clinical setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4582. doi:10.1158/1538-7445.AM2017-4582

Abstract 661: Evaluating "harmonization" of PD-L1 assays using image analysis

Abstract The current diagnostics landscape for therapeutics that target the PD1/PD-L1 pathway is highly complex. Four different companion or complementary diagnostics have been developed for pembrolizumab in NSCLC, nivolumab in NSCLC and melanoma, and atezolizumab in urothelial carcinoma. The need to reconcile diagnostics for this class of targeted therapies has been recognized by the creation of the FDA-AACR-ASCO “PD-L1 Blueprint” working group to explore means to “harmonize” PD-L1 testing in tissue based IHC assays. The results reported from this working group noted similarities, but also several important discrepancies, between the current assays. Since each test uses a specific interpretation for each assay and indication, this creates a highly complex diagnostic landscape, which is likely to continue to increase in complexity as more PD-1/PD-L1 therapeutics and potentially novel diagnostics continue to be approved in additional indications. To address the need for adaptive, sustainable harmonization for PD-L1 diagnostics, Flagship Biosciences evaluated the utility of image analysis-based methods to harmonize multiple PD-L1 tests. We executed a proof-of-concept study utilizing a cohort of serial tissue sections from the same NSCLC patients, stained with the FDA approved Dako PD-L1 tests (28-8 and 22C3 clones), and our in-house PD-L1 assays (SP142 and E1L3N clones) for comparison. We digitized the tissue slides using a whole slide scanner, and evaluated the samples with our tissue Image Analysis (tIA™) technology. As expected, the patient samples stained with the separate PD-L1 assays yielded differences in staining and, thus, the reported scores for PD-L1 expression based on each test used, despite serial sections being derived from the same patient. To attempt to harmonize the scoring approaches for each test, we leveraged our computational Tissue Analysis (cTA™) platform to create a mathematically-derived “virtual slide score” for each sample, which enabled calibration of the various tests to deliver cross PD-L1 comparative scores. Based on the proof-of-concept demonstrated in this study, the cTA™ approaches could enable harmonization of the various PD-L1 tests through use of a digital pathology platform. The data presented provides a foundation for potential application of the cTA™ platform in the clinical laboratory setting to achieve harmonization of multiple PD-L1 tests. Citation Format: Nathan T. Martin, Joshua C. Black, Zachary Pollack, Famke Aeffner, Joseph Krueger. Evaluating "harmonization" of PD-L1 assays using image analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 661. doi:10.1158/1538-7445.AM2017-661

Quantification of dystrophin to assess the effects of dystrophin-restoring treatments in Duchenne muscular dystrophy: Lessons from the development of eteplirsen

Objective: Accurate assessment of dystrophin expression is essential in evaluating effects of dystrophin-restoring therapies. Eteplirsen was recently approved for patients with Duchenne muscular dystrophy (DMD) amenable to exon 51 skipping based on an observed increase in dystrophin. This analysis used 3 complementary methods for dystrophin protein quantification in muscle tissue at baseline and following eteplirsen treatment at Week 180 of a clinical trial (NCT01540409). Methods: Three well-controlled methods were used to assess changes from baseline in dystrophin: Western blot analysis to quantify total dystrophin protein (Sarepta), pathologist scoring of immunofluorescent staining to determine percent dystrophin-positive fibers (PDPF; Flagship Biosciences, NCH), and immunofluorescence to assess dystrophin-associated fiber intensity (NCH). All analyses were performed using blinded conditions. The validated Western blot method was quantitative and optimized to a low linear dynamic range expected for DMD, with no saturating signal. Reproducible evaluation of PDPF analysis, as evidenced by high interrater reliability, was performed independently by 3 trained pathologists (Flagship Biosciences) using standardized viewing conditions and scoring requirements. Pathologists effectively demonstrated and maintained concordance throughout scoring. Analysis of immunofluorescent dystrophin fiber intensity was performed on 4 systematic, random subsample images of each tissue section using BIOQUANT™ software. Results: In total, 20 biopsies were evaluated (eteplirsen patients, n=11; untreated DMD controls, n=9). At Week 180, compared with untreated control biopsies, significantly greater dystrophin expression was observed in eteplirsen-treated patient samples by Western blot (absolute difference, +0.85%; 11.6-fold greater; P=.007), PDPF manual scoring (absolute difference, +16.3%; 15.5-fold greater; P<.001), and dystrophin immunofluorescent fiber intensity (absolute difference, +13.2%; 2.4-fold greater; P<.001). Conclusion: Our findings establish the importance of using a combination of carefully optimized, complementary methods for dystrophin quantification in muscle biopsies of DMD patients. Refinements of the methods are discussed, with the goal of improving sensitivity and reducing potential bias.

Abstract 4042: Quantifying immune oncology markers across multiple tissue sections with digital image analysis

Abstract Immuno-oncology (IO) approaches requires an understanding of the types of immune cells present in the tumor microenvironment (TME) as well as the precise localization of these immune cells. However, the acquisition of spatial IO information is technically challenging, due to the requirement for multiplex labeling of immune cells and the need to categorize their location and biomarker content simultaneously. Additionally, the multiplex biomarker panel must be engineered in advance based on a priori assumptions about the correct marker combinations and their location (such as the tumor epithelial nests, TME, or specifically the tumor-stroma interface). This limits the ability to implement novel, scientifically driven assessments into an existing clinical trial which already has defined immunohistochemistry endpoints. To meet the needs of IO clinical trials, Flagship Biosciences has developed a proprietary image analysis platform, FACTS (Feature Analysis on Complementary Tissue Sections), to deduce both the necessary multiplex staining information and the spatial context of IO markers based on the utilization of existing monochrome or multiplex stained slides from a clinical trial. In this study, we demonstrate the utility of this approach to deliver biologically relevant endpoints important for IO clinical trials. First, we performed single IO biomarker staining for CD4, CD8 and FoxP3 in colorectal cancer patient samples and developed a novel image analysis approach that allowed for the accurate quantification of multiple IO markers within specific margins of the tumor microenvironment across multiple tissue sections. For each biomarker the positive cell populations were binned based on distance from the tumor-TME boundary (i.e.% positive cells within 100μM, 200μM or 500μM of the tumor-TME boundary). Next, we used computational alignment for evaluating the co-localization of multiple immune cell biomarkers from serial sections with single stains for CD4, CD8 or FoxP3. We demonstrate that the information extracted from multiple single stained serial sections can be used to generate co-localization information for multiple IO markers in a given patient sample. Lastly, we developed multiplex chromogenic assays for these same markers, analyzed the multiplex-stained slides with image analysis, and derived the same analysis endpoints for the multiplex slides to the digitally aligned individually labeled sections. The study found that similar interpretation of the inflammatory landscape was possible with multiplex-stained slides and digital alignment of monoplex-stained slides. In summary, this study demonstrated a novel image analysis approach that allows for quantification of IO markers utilizing clinically relevant wet assay technologies and existing IHC stained slides derived from an immune-oncology clinical trial. Citation Format: Ciara A. Martin, Joshua Black, Nathan T. Martin, Logan Cerkovnik, Jasmeet Bajwa, Kristin Wilson, Daniel Rudmann, Carsten Schnatwinkel, AJ Milici. Quantifying immune oncology markers across multiple tissue sections with digital image analysis. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4042.

Abstract 4162: Identifying T lymphocytes in IHC-stained tissues independently of CD3+ staining using morphometric features extracted by image analysis

Abstract Assessments of leukocyte populations in the context of cancer tissues are typically determined by staining for leukocyte subtype markers in formalin fixed tissues. This requires the identification, categorization, and localization of multiple leukocytes within tissue context utilizing multiple markers. Meeting these demands are challenging due to technical constraints on the number of individual markers which can be visualized or scored in a single slide, and the complexity of staining observed. The use of multispectral imaging with fluorescent multiplexed markers has better enabled the assessment of multiple markers in a single tissue section. However, these wet chemistry and image capture technologies are very complex, and can only be successfully implemented with significant laboratory infrastructure, specialty equipment, and experienced resources. For these reasons, this approach is not widely implementable in the clinical pathology laboratory setting, where such tests are oriented to a companion diagnostic utility. Approaches which rely on widely adopted chromogenic immunohistochemistry (IHC) staining are preferred in the clinical laboratory setting, but the number of assayable markers is limited to 1-3 unique markers in a single tissue. In order to create a useful approach that could be implemented in existing clinical laboratory workflow, Flagship Biosciences has developed an approach for deriving the complex endpoints often necessary in immuno-oncology studies which rely on 1-3 chromagenic stains and computer interpretation of the tissue using only hematoxylin counterstain to identify T-lymphocytes. In a proof-of-concept study, we utilized our Tissue Image Analysis (TIA) tools to identify morphometric parameters which could identify T-lymphocytes, independent of staining for the T-lymphocyte marker CD3. A cohort of non-small cell lung cancer (NSCLC) tissues was stained by CD3 IHC, and both CD3 and isotype-stained tissues were analyzed with Flagship's CellMap™ software to capture the morphometric and staining features of cells in the tissues. The morphometric features which characterized CD3+ cells were used to approximate the T-lymphocyte population frequency in the isotype-stained tissues. This T-lymphocyte classification scheme was defined based on hematoxylin staining alone, and accuracy of T-lymphocyte classification was verified by CD3 staining. Based on this study, the method described herein could be utilized to reasonably estimate the frequency of T-lymphocyte subsets (e.g. CD4+, CD8+, etc.) or different marker-positive leukocyte (e.g. macrophages) subsets relative to the total T-lymphocyte population without an additional T-lymphocyte marker such as CD3. The approach could, therefore, provide an added dimension of analysis for tissues stained by IHC without adding complexity to the wet assay by necessitating a marker for T-lymphocytes. Citation Format: Nathan T. Martin, Joshua Black, Famke Aeffner, Logan Cerkovnik, Jasmeet Bajwa, Staci J. Kearney, Crystal Pulliam, A.J. Milici, Joseph Krueger. Identifying T lymphocytes in IHC-stained tissues independently of CD3+ staining using morphometric features extracted by image analysis. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4162.

Abstract 2548: Using whole slide digital image analysis to quantify leukocyte populations in tumor sections

Abstract In recent years, the tumor microenvironment (TME) has been identified as an important factor influencing the growth and metastasis of the tumor. Multiple studies have shown that adding the “Immunoscore” assessment to the AJCC/UICC-TNM classification system improves the accuracy of outcome prediction, and in some cases outperforms traditional TNM staging. In many instances these studies have been performed utilizing 2-3 independent readers to manually quantify the cells, performed on selective high-powered fields or TMA cores rather than the entire specimen, resulting in variations in counts when different high powered fields (HPFs) or cores are chosen. A key method to increase the throughput and to decrease the variability is to utilize whole slide imaging and computerized image analysis to provide leukocyte counts. An image analysis algorithm which can automatically differentiate tumor from stroma would allow rapid quantification of endpoints in each tissue compartment across the whole specimen. To address these issues, Flagship Biosciences has designed proprietary CellMapTM image analysis algorithm tools to develop an Immunoscore-like paradigm for colorectal cancers (CRCs) to potentially provide new and more accurate TME information to aid in interpretation. Utilizing whole slide imaging (WSI) approaches, CellMap™ allows the quantitation of leukocyte populations (e.g., CD3+, CD8+, FoxP3+) automatically across whole tissue sections. Using this algorithm, leukocyte populations were quantified in sections that have been either singly or dually labeled for inflammatory markers. Our preliminary studies indicate that Immunoscore-like scoring paradigm should be established both in tumor areas and in adjacent stroma to provide the most complete information on the biology of the tumor. We compared the use of this approach in tissue microarray (TMA) cores which generally sample areas of dense tumor mass, and compared automated WSI to manual HPF approaches. Accuracy of the algorithm was demonstrated by comparing data from manual counts to algorithm derived counts using high-powered fields. These data support using CellMap™ in the prospective or retrospective assessment of leukocyte subpopulations in whole slides of clinical samples. This approach will diminish variability in counting, expand the types of endpoints determined, and improve the statistical value of these determinations, thereby facilitating robust TME measurements with clinical value. Note: This abstract was not presented at the meeting. Citation Format: Joseph S. Krueger, Brian Laffin, Holger Lange, Anthony Milici, Eric Neeley, Mirza Peljto, Mahipal Suraneni, David Young. Using whole slide digital image analysis to quantify leukocyte populations in tumor sections. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2548. doi:10.1158/1538-7445.AM2014-2548

Abstract 5426: Assessing factors predictive of response to ADCs for companion diagnostic strategies

Abstract One premise of antibody-drug conjugates (ADC) is that the bound mAb-antigen complex on the cell surface will internalize and be metabolized by lysosomal proteases to release the free drug. Thus, the efficacy of an ADC is dependent not only on the presence of cell surface antigens, but also in-tact delivery of the conjugated drug, and an active pathway of receptor-mediated endocytosis. On a cellular level, the biological mechanisms behind receptor internalization and turnover have not been elucidated for novel therapeutic targets. On a tissue level, vascularization and hypoxic profile will affect delivery of the antibody into the tissue. Varying expression of the antibody target within the tissue (antigen density) will also affect the uptake of the ADC. Furthermore, the extracellular stability of the ADC may be affected by the activity of the various proteases in the tumor microenvironment (TME), outside of the lysozome. These concepts are critical for efficacy, and thus are especially important for companion diagnostic approaches (CDx) meant to predict response to ADCs. Thus, a predictive assay which would account not only for the degree of cell surface expression of the target, but also receptor internalization and potential effects of tumor microenvironment is required. To answer this need, Flagship Biosciences has invented several proprietary approaches for measuring critical properties of the therapeutic target on the cell surface or inside the cell, as well as properties of the TME which could be used to understand and predict efficacy to an ADC using FFPE biopsies. These quantitative pathology approaches are based on image analysis approaches which been designed specifically for ADC CDx programs to answer these critical questions: 1) Defining cell surface target expression independent of cytoplasmic expression; 2) Estimating receptor flux (turnover, internalization) based on staining profile; 3) Assessing critical factors in the TME which may affect delivery of the in-tact drug to the intracellular target; 4) Assaying vascular properties which may affect delivery of the drug; and 5) Heterogeneity of the target within a tumor. We are able to provide these as discrete evaluations or multiplex these evaluations for an integrative answer which can be derived from a typical clinical biopsy. Incorporation of these novel tools will enable ADC developers to create efficacy and patient stratification paradigms which incorporate the critical biological endpoints unique to ADCs. Citation Format: Joseph S. Krueger, Holger Lange, Steve Potts, David Young. Assessing factors predictive of response to ADCs for companion diagnostic strategies. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5426. doi:10.1158/1538-7445.AM2014-5426

The effect of SETD2 mutation (mts) on histone 3 lysine 36 tri-methylation (H3K36me3) and correlation with clinical outcome in patients (pts) with metastatic clear cell renal cell carcinoma (ccRCC) enrolled in COMPARZ.

4583 Background: Systematic sequencing studies of ccRCC identified loss of function mts in histone modifying enzymes such as SETD2 (15%). All SETD2 mts identified in pts disrupt expression of the SET domain, a conserved motif responsible for H3K36 tri-methyltransferase activity. In this study, we hypothesized that SETD2mts are associated with decreased H3K36me3 in ccRCC and we also investigate this association in relation with clinical outcome. Methods: Tumor samples were collected from consented subjects enrolled in a phase III trial of pazopanib vs. sunitinib (Motzer, NEJM 2013). Immunohistochemistry (IHC) for H3K36me3 was optimized with corresponding isotype controls. The Aperio platform was used to scan the slides for analysis of nuclear H3K36me3 levels using the CellMap image algorithm by Flagship Biosciences on 46 subjects with known SETD2 gene status by next generation sequencing. H3K36me3 signal was scored according to an H-score. Results: IHC shows a decrease in H3K36me3 staining in the tumor cel...