Reshaping the Tumor Microenvironment of KRASG12D Pancreatic Ductal Adenocarcinoma with combined SOS1 and MEK Inhibition for Improved Immunotherapy Response.

KRAS inhibitors have demonstrated exciting pre-clinical and clinical responses, although resistance occurs rapidly. Here, we investigate the effects of KRAS-targeting therapies on the tumor microenvironment using a library of KRASG12D, p53 mutant, murine PDAC-derived cell lines (KPCY) to leverage immune-oncology combination strategies for long-term tumor efficacy. Our findings show that SOS1 and MEK inhibitors (SOS1i+MEKi) suppressed tumor growth in syngeneic models and increased intra-tumoral CD8+ T cells without durable responses. scRNA-sequencing revealed an increase in inflammatory cancer associated fibroblasts (iCAFs), M2 macrophages, and a decreased dendritic cell quality that ultimately resulted in a highly immunosuppressive microenvironment driven by IL6+ iCAFs. Agonist CD40 treatment was effective to revert macrophage polarization and overcome the lack of mature antigen presenting DCs after SOS1i+MEKi therapy. Treatment increased the overall survival of KPCY tumor-bearing mice. The addition of checkpoint blockade to SOS1i+MEKi combination resulted in tumor free mice with established immune memory. Our data suggests that KRAS inhibition affects myeloid cell maturation and highlights the need for combining KRAS cancer-targeted therapy with myeloid activation to enhance and prolong anti-tumor effects.

Digital spatial profiling of melanoma shows CD95 expression in immune cells is associated with resistance to immunotherapy.

Although immune checkpoint inhibitor (ICI) therapy has dramatically improved outcome for metastatic melanoma patients, many patients do not benefit. Since adverse events may be severe, biomarkers for resistance would be valuable, especially in the adjuvant setting. We performed high-plex digital spatial profiling (DSP) using the NanoString GeoMx® on 53 pre-treatment specimens from ICI-treated metastatic melanoma cases. We interrogated 77 targets simultaneously in four molecular compartments defined by S100B for tumor, CD68 for macrophages, CD45 for leukocytes, and nonimmune stromal cells defined as regions negative for all three compartment markers but positive for SYTO 13. For DSP validation, we confirmed the results obtained for some immune markers, such as CD8, CD4, CD20, CD68, CD45, and PD-L1, by quantitative immunofluorescence (QIF). In the univariable analysis, 38 variables were associated with outcome, 14 of which remained significant after multivariable adjustment. Among them, CD95 was further validated using multiplex immunofluorescence in the Discovery immunotherapy (ITX) Cohort and an independent validation cohort with similar characteristics, showing an association between high levels of CD95 and shorter progression-free survival. We found that CD95 in stroma was associated with resistance to ICI. With further validation, this biomarker could have value to select patients that will not benefit from immunotherapy.

Subsets of IFN Signaling Predict Response to Immune Checkpoint Blockade in Patients with Melanoma.

PURPOSE: IFN signaling in the tumor microenvironment is a critical determinant of both response and resistance of cancer to immune checkpoint inhibitors (ICI). We hypothesized that distinct patterns of IFN signaling in melanoma are associated with clinical response or resistance to ICIs. EXPERIMENTAL DESIGN: Two tissue microarrays containing samples from 97 patients with metastatic melanoma who received nivolumab, pembrolizumab, or a combination of ipilimumab and nivolumab at Yale New Haven Hospital between 2011 and 2017 were randomized into discovery and validation cohorts. Samples were stained and visualized using multiplexed immunofluorescence microscopy for STAT1, STAT1 phosphorylated at Y701 (pSTAT1Y701), and PD-L1, and signals were quantified using the automated quantitative analysis method of quantitative immunofluorescence. Treatment response was assessed using RECIST, and overall survival was analyzed. For in vitro studies, human melanoma cell lines were stimulated with IFNγ and IFNß, and Western blotting was performed. RESULTS: Pretreatment STAT1 levels were higher in responders to ICIs [complete response/partial response/stable disease (SD) for > 6 months] than in nonresponders (SD < 6 months/progressive disease). Higher pretreatment STAT1 levels were associated with improved survival after ICIs in both the discovery and validation cohorts. Western blot analysis of human melanoma cell lines stimulated with IFN demonstrated distinct patterns of upregulation of STAT1 compared with pSTAT1Y701 and PD-L1. When combining STAT1 and PD-L1 markers, patients with STAT1highPD-L1low tumors had improved survival compared with those with STAT1lowPD-L1high tumors. CONCLUSIONS: STAT1 may better predict melanoma response to ICIs than current strategies, and combined STAT1 and PD-L1 biomarkers may provide insight into IFN-responsive versus IFN-resistant states.

Baseline gene expression profiling determines long-term benefit to programmed cell death protein 1 axis blockade.

Treatment with immune checkpoint inhibitors has altered the course of malignant melanoma, with approximately half of the patients with advanced disease surviving for more than 5 years after diagnosis. Currently, there are no biomarker methods for predicting outcome from immunotherapy. Here, we obtained transcriptomic information from a total of 105 baseline tumor samples comprising two cohorts of patients with advanced melanoma treated with programmed cell death protein 1 (PD-1)-based immunotherapies. Gene expression profiles were correlated with progression-free survival (PFS) within consecutive clinical benefit intervals (i.e., 6, 12, 18, and 24 months). Elastic net binomial regression models with cross validation were utilized to compare the predictive value of distinct genes across time. Lasso regression was used to generate a signature predicting long-term benefit (LTB), defined as patients who remain alive and free of disease progression at 24 months post treatment initiation. We show that baseline gene expression profiles were consistently able to predict long-term immunotherapy outcomes with high accuracy. The predictive value of different genes fluctuated across consecutive clinical benefit intervals, with a distinct set of genes defining benefit at 24 months compared to earlier outcomes. A 12-gene signature was able to predict LTB following anti-PD-1 therapy with an area under the curve (AUC) equal to 0.92 and 0.74 in the training and validation set, respectively. Evaluation of LTB, via a unique signature may complement objective response classification and characterize the logistics of sustained antitumor immune responses.

Spatially resolved proteomic profiling identifies tumor cell CD44 as a biomarker associated with sensitivity to PD-1 axis blockade in advanced non-small-cell lung cancer.

BACKGROUND: Most patients with advanced non-small-cell lung cancer (NSCLC) fail to derive significant benefit from programmed cell death protein-1 (PD-1) axis blockade, and new biomarkers of response are needed. In this study, we aimed to discover and validate spatially resolved protein markers associated with sensitivity to PD-1 axis inhibition in NSCLC. METHODS: We initially assessed a discovery cohort of 56 patients with NSCLC treated with PD-1 axis inhibitors at Yale Cancer Center. Using the GeoMx Digital Spatial Profiling (DSP) system, 71 proteins were measured in spatial context on each spot in a tissue microarray. We used the AQUA method of quantitative immunofluorescence (QIF) to orthogonally validate candidate biomarkers. For external independent validation, we assessed whole tissue sections derived from 128 patients with NSCLC treated with single-agent PD-1 axis inhibitors at the 12 de Octubre Hospital (Madrid) using DSP. We further analyzed two immunotherapy untreated cohorts to address prognostic significance (n=252 from Yale Cancer Center; n=124 from University Clinic of Navarra) using QIF and DSP, respectively. RESULTS: Using continuous log-scaled data, we identified CD44 expression in the tumor compartment (pan-cytokeratin (CK)+) as a novel predictor of prolonged progression-free survival (PFS) (multivariate HR=0.68, p=0.043) in the discovery set. We validated by QIF that tumor CD44 levels assessed as continuous QIF scores were associated with longer PFS (multivariate HR=0.31, p=0.022) and overall survival (multivariate HR=0.29, p=0.038). Using DSP in an independent immunotherapy treated cohort, we validated that CD44 levels in the tumor compartment, but not in the immune compartment (panCK-/CD45+), were associated with clinical benefit (OR=1.22, p=0.018) and extended PFS under PD-1 axis inhibition using the highest tertile cutpoint (multivariate HR=0.62, p=0.03). The effect of tumor cell CD44 in predicting PFS remained significant after correcting for programmed death-ligand 1 (PD-L1) Tumor Proportion Score (TPS) in both cohorts. High tumor cell CD44 was not prognostic in the absence of immunotherapy. Using DSP data, intratumoral regions with elevated tumor cell CD44 expression showed prominent (fold change>1.5, adjusted p<0.05) upregulation of PD-L1, TIM-3, ICOS, and CD40 in two independent cohorts. CONCLUSIONS: This work highlights CD44 as a novel indicative biomarker of sensitivity to PD-1 axis blockade that might help to improve immunotherapy strategies for NSCLC.

An In Vivo Model for Elucidating the Role of an Erythroid-Specific Isoform of Nuclear Export Protein Exportin 7 (Xpo7) in Murine Erythropoiesis.

Erythroid nuclear condensation is a complex process in which compaction to one-tenth its original size occurs in an active nucleus simultaneously undergoing transcription and cell division. We previously found that the nuclear exportin Exportin7 (Xpo7), which is erythroid- specific and highly induced during terminal erythropoiesis, facilitates nuclear condensation. We also identified a previously unannotated, erythroid-specific isoform of Xpo7 (Xpo7B) containing a novel first exon Xpo7-1b expressed only in late Ter119+ erythroblasts. To better understand the functional difference between the erythroid Xpo7B isoform and the ubiquitous isoform (Xpo7A) containing the original first exon Xpo7-1a, we created gene-targeted mouse models lacking either exon Xpo7-1a or Xpo7-1b, or both exons 4 and 5, which are completely null for Xpo7 expression. We found that deficiency in Xpo7A does not affect steady-state nor stress erythropoiesis. In contrast, mice lacking the erythroid isoform, Xpo7B, exhibit a mild anemia as well as altered stress erythropoiesis. Complete Xpo7 deficiency resulted in partially penetrant embryonic lethality at the stage when definitive erythropoiesis is prominent in the fetal liver. Inducible complete knockdown of Xpo7 confirms that both steady-state erythropoiesis and stress erythropoiesis are affected. We also observe that Xpo7 deficiency downregulates the expression of important stress response factors, such as Gdf15 and Smad3. We conclude that the erythroid-specific isoform of Xpo7 is important for both steady-state and stress erythropoiesis in mice.

Objective assessment of tumor infiltrating lymphocytes as a prognostic marker in melanoma using machine learning algorithms.

BACKGROUND: The prognostic value of tumor-infiltrating lymphocytes (TILs) assessed by machine learning algorithms in melanoma patients has been previously demonstrated but has not been widely adopted in the clinic. We evaluated the prognostic value of objective automated electronic TILs (eTILs) quantification to define a subset of melanoma patients with a low risk of relapse after surgical treatment. METHODS: We analyzed data for 785 patients from 5 independent cohorts from multiple institutions to validate our previous finding that automated TIL score is prognostic in clinically-localized primary melanoma patients. Using serial tissue sections of the Yale TMA-76 melanoma cohort, both immunofluorescence and Hematoxylin-and-Eosin (H&E) staining were performed to understand the molecular characteristics of each TIL phenotype and their associations with survival outcomes. FINDINGS: Five previously-described TIL variables were each significantly associated with overall survival (p<0.0001). Assessing the receiver operating characteristic (ROC) curves by comparing the clinical impact of two models suggests that etTILs (electronic total TILs) (AUC: 0.793, specificity: 0.627, sensitivity: 0.938) outperformed eTILs (AUC: 0.77, specificity: 0.51, sensitivity: 0.938). We also found that the specific molecular subtype of cells representing TILs includes predominantly cells that are CD3+ and CD8+ or CD4+ T cells. INTERPRETATION: eTIL% and etTILs scores are robust prognostic markers in patients with primary melanoma and may identify a subgroup of stage II patients at high risk of recurrence who may benefit from adjuvant therapy. We also show the molecular correlates behind these scores. Our data support the need for prospective testing of this algorithm in a clinical trial. FUNDING: This work was also supported by a sponsored research agreements from Navigate Biopharma and NextCure and by grants from the NIH including the Yale SPORE in in Skin Cancer, P50 CA121974, the Yale SPORE in Lung Cancer, P50 CA196530, NYU SPORE in Skin Cancer P50CA225450 and the Yale Cancer Center Support Grant, P30CA016359.

Programmed Death-Ligand 1 and Programmed Death-Ligand 2 mRNAs Measured Using Closed-System Quantitative Real-Time Polymerase Chain Reaction Are Associated With Outcome and High Negative Predictive Value in Immunotherapy-Treated NSCLC.

INTRODUCTION: Immune checkpoint inhibitors (ICIs) have become standard of care in lung cancer management, but only a relatively small percentage of patients treated respond. Current predictive biomarkers, including immunohistochemical detection of programmed death-ligand 1 (PD-L1), are insufficient for determining who will respond or, more importantly in the adjuvant setting, who will not respond to ICI therapy. Here, we investigate an alternative method of assessment of PD-L1 to predict nonresponse. METHODS: This study uses a research use only quantitative real-time reverse transcription polymerase chain reaction assay on the GeneXpert system, to test for the association between four target immune genes, CD274 (PD-L1), PDCD1LG2 (programmed death-ligand 2 [PD-L2]), CD8A, and IRF1, and response to ICI therapy. Tissues were collected from 122 patients with advanced NSCLC before ICI therapy in a retrospective cohort, macrodissected, and analyzed using the GeneXpert. RESULTS: Both high PD-L1 and PD-L2 mRNA expression levels were associated with improved long-term benefit at 24 months (p = 0.047 for both PD-L1 and PD-L2) and overall survival (PD-L1, p = 0.048; PD-L2, p = 0.049). Both PD-L1 and PD-L2 mRNA levels were higher in patients with KRAS mutations. Most importantly, low PD-L1 mRNA level had a high negative predictive value of 0.92 for absence of long-term benefit. CONCLUSIONS: With further validation of this assay in low-stage patients, an assessment of PD-L1 mRNA rather than protein, could be a method to determine which low-stage patients that should not be treated with ICIs in the adjuvant setting. This approach may also be a useful objective method for selecting patients for treatment in the advanced setting.

Discovery of Biomarkers of Resistance to Immune Checkpoint Blockade in NSCLC Using High-Plex Digital Spatial Profiling.

INTRODUCTION: Despite the clinical efficacy of immune checkpoint inhibitors (ICIs) in NSCLC, only approximately 20% of patients remain disease-free at 5 years. Here, we use digital spatial profiling to find candidate biomarker proteins associated with ICI resistance. METHODS: Pretreatment samples from 56 patients with NSCLC treated with ICI were analyzed using the NanoString GeoMx digital spatial profiling method. A panel of 71 photocleavable oligonucleotide-labeled primary antibodies was used for protein detection in four molecular compartments (tumor, leukocytes, macrophages, and immune stroma). Promising candidates were orthogonally validated with quantitative immunofluorescence. Available pretreatment samples from 39 additional patients with NSCLC who received ICI and 236 non-ICI-treated patients with operable NSCLC were analyzed to provide independent cohort validation. RESULTS: Biomarker discovery using the protein-based molecular compartmentalization strategy allows 284 protein variables to be assessed for association with ICI resistance by univariate analysis using continuous log-scaled data. Of the 71 candidate protein biomarkers, CD66b in the CD45+CD68 molecular compartment (immune stroma) predicted significantly shorter overall survival (OS) (hazard ratio [HR] 1.31, p = 0.016) and was chosen for validation. Orthogonal validation by quantitative immunofluorescence illustrated that CD66b was associated with resistance to ICI therapy but not prognostic for poor outcomes in untreated NSCLC (discovery cohort [OS HR 2.49, p = 0.026], validation cohort [OS HR 2.05, p = 0.046], non-ICI-treated cohort [OS HR 1.67, p = 0.06]). CONCLUSIONS: Using the technique, we have discovered that CD66b expression is indicative of resistance to ICI therapy in NSCLC. Given that CD66b identifies neutrophils, further studies are warranted to characterize the role of neutrophils in ICI resistance.

Quantitative measurement of HER2 expression to subclassify ERBB2 unamplified breast cancer.

The efficacy of the antibody drug conjugate (ADC) Trastuzumab deruxtecan (T-DXd) in HER2 low breast cancer patients suggests that the historical/conventional assays for HER2 may need revision for optimal patient care. Specifically, the conventional assay is designed to distinguish amplified HER2 from unamplified cases but is not sensitive enough to stratify the lower ranges of HER2 expression. Here we determine the optimal dynamic range for unamplified HER2 detection in breast cancer and then redesign an assay to increase the resolution of the assay to stratify HER2 expression in unamplified cases. We used the AQUA™ method of quantitative immunofluorescence to test a range of antibody concentrations to maximize the sensitivity within the lower range of HER2 expression. Then, using a cell line microarray with HER2 protein measured by mass spectrometry we determined the amount of HER2 protein in units of attomols/mm2. Then by calculation of the limits of detection, quantification, and linearity of this assay we determined that low HER2 range expression in unamplified cell lines is between 2 and 20 attomol/mm2. Finally, application of this assay to a serial collection of 364 breast cancer cases from Yale shows 67% of the population has HER2 expression above the limit of quantification and below the levels seen in HER2 amplified breast cancer. In the future, this assay could be used to determine the levels of HER2 required for response to T-DXd or similar HER2 conjugated ADCs.

An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer.

PURPOSE: Although tumor-infiltrating lymphocytes (TIL) assessment has been acknowledged to have both prognostic and predictive importance in triple-negative breast cancer (TNBC), it is subject to inter and intraobserver variability that has prevented widespread adoption. Here we constructed a machine-learning based breast cancer TIL scoring approach and validated its prognostic potential in multiple TNBC cohorts. EXPERIMENTAL DESIGN: Using the QuPath open-source software, we built a neural-network classifier for tumor cells, lymphocytes, fibroblasts, and "other" cells on hematoxylin-eosin (H&E)-stained sections. We analyzed the classifier-derived TIL measurements with five unique constructed TIL variables. A retrospective collection of 171 TNBC cases was used as the discovery set to identify the optimal association of machine-read TIL variables with patient outcome. For validation, we evaluated a retrospective collection of 749 TNBC patients comprised of four independent validation subsets. RESULTS: We found that all five machine TIL variables had significant prognostic association with outcomes (P ≤ 0.01 for all comparisons) but showed cell-specific variation in validation sets. Cox regression analysis demonstrated that all five TIL variables were independently associated with improved overall survival after adjusting for clinicopathologic factors including stage, age, and histologic grade (P ≤ 0.0003 for all analyses). CONCLUSIONS: Neural net-driven cell classifier-defined TIL variables were robust and independent prognostic factors in several independent validation cohorts of TNBC patients. These objective, open-source TIL variables are freely available to download and can now be considered for testing in a prospective setting to assess clinical utility.See related commentary by Symmans, p. 5446.

A new tool for technical standardization of the Ki67 immunohistochemical assay.

Ki67, a nuclear proliferation-related protein, is heavily used in anatomic pathology but has not become a companion diagnostic or a standard-of-care biomarker due to analytic variability in both assay protocols and interpretation. The International Ki67 Working Group in breast cancer has published and has ongoing efforts in the standardization of the interpretation of Ki67, but they have not yet assessed technical issues of assay production representing multiple sources of variation, including antibody clones, antibody formats, staining platforms, and operators. The goal of this work is to address these issues with a new standardization tool. We have developed a cell line microarray system in which mixes of human Karpas 299 or Jurkat cells (Ki67+) with Sf9 (Spodoptera frugiperda) (Ki67-) cells are present in incremental standardized ratios. To validate the tool, six different antibodies, including both ready-to-use and concentrate formats from six vendors, were used to measure Ki67 proliferation indices using IHC protocols for manual (bench-top) and automated platforms. The assays were performed by three different laboratories at Yale and analyzed using two image analysis software packages, including QuPath and Visiopharm. Results showed statistically significant differences in Ki67 reactivity between each antibody clone. However, subsets of Ki67 assays using three clones performed in three different labs show no significant differences. This work shows the need for analytic standardization of the Ki67 assay and provides a new tool to do so. We show here how a cell line standardization system can be used to normalize the staining variability in proliferation indices between different antibody clones in a triple negative breast cancer cohort. We believe that this cell line standardization array has the potential to improve reproducibility among Ki67 assays and laboratories, which is critical for establishing Ki67 as a standard-of-care assay.

Biomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass Cytometry.

PURPOSE: Imaging mass cytometry (IMC) is among the first tools with the capacity for multiplex analysis of more than 40 targets, which provides a novel approach to biomarker discovery. Here, we used IMC to characterize the tumor microenvironment of patients with metastatic melanoma who received immunotherapy in efforts to find indicative factors of treatment response. In spite of the new power of IMC, the image analysis aspects are still limited by the challenges of cell segmentation. EXPERIMENTAL DESIGN: Here, rather than segment, we performed image analysis using a newly designed version of the AQUA software to measure marker intensity in molecularly defined compartments: tumor cells, stroma, T cells, B cells, and macrophages. IMC data were compared with quantitative immunofluorescence (QIF) and digital spatial profiling. RESULTS: Validation of IMC results for immune markers was confirmed by regression with additional multiplexing methods and outcome assessment. Multivariable analyses by each compartment revealed significant associations of 12 markers for progression-free survival and seven markers for overall survival (OS). The most compelling indicative biomarker, beta2-microglobulin (B2M), was confirmed by correlation with OS by QIF in the discovery cohort and validated in an independent published cohort profiled by mRNA expression. CONCLUSIONS: Using digital image analysis based on pixel colocalization to assess IMC data allowed us to quantitively measure 25 markers simultaneously on formalin-fixed, paraffin-embedded tissue microarray samples. In addition to showing high concordance with other multiplexing technologies, we identified a series of potentially indicative biomarkers for immunotherapy in metastatic melanoma, including B2M.

Antibody validation for protein expression on tissue slides: a protocol for immunohistochemistry.

Antibodies play a crucial role in basic research and clinical decision-making. However, there are no standardized algorithms or guidelines to ensure their accuracy and validity. There have been efforts to generate consensus, but, with the exception of clinical labs, antibody validation remains variable in the literature and sometimes in clinical practice. Here we focus on immunohistochemistry, an example of a scientific and clinical tool where validation of antibodies is critical. We describe a protocol that we use to validate antibodies specifically for immunohistochemistry, including some of the pillars of antibody validation from Uhlen et al. 2016, as an example of a rigorous approach to build antibody-based tests for both basic and translational science labs and for the clinic.

Digital quantitative assessment of PD-L1 using digital spatial profiling.

The assessment of programmed death 1 ligand 1 (PD-L1) expression by Immunohistochemistry (IHC) is the US Food and Drug Administration (FDA)-approved predictive marker to select responders to checkpoint blockade anti-PD-1/PD-L1 axis immunotherapies. Different PD-L1 immunohistochemistry (IHC) assays use different antibodies and different scoring methods in tumor cells and immune cells. Multiple studies have compared the performance of these assays with variable results. Here, we investigate an alternative method for assessment of PD-L1 using a new technology known as digital spatial profiling. We use a previously described standardization tissue microarray (TMA) to assess the accuracy of the method and compare digital spatial profiler (DSP) to each FDA-approved PD-L1 assays, one LDT assay and three quantitative fluorescence assays. The standardized cell line Index tissue microarray contains 10 isogenic cells lines in triplicates expressing various ranges of PD-L1. The dynamic range of PD-L1 digital counts was measured in the ten cell lines on the Index TMA using the GeoMx DSP assay and read on the nCounter platform. The digital method shows very high correlation with immunohistochemistry scored with quantitative software and with quantitative fluorescence. High correlation of PD-L1 digital DSP counts were seen between rows on the same Index TMA. Finally, experiments from two Index TMAs showed reproducibility of DSP counts were independent of variable slide storage time over a three-week period after antibody labeling but before collection of cleaved tags. In summary, DSP appears to have quantitative potential comparable to quantitative immunohistochemistry. It is possible that this technology could be used as a PD-L1 protein measurement system for companion diagnostic testing for immune therapy.

Advances in quantitative immunohistochemistry and their contribution to breast cancer.

Introduction: Automated image analysis provides an objective, quantitative, and reproducible method of measurement of biomarkers. Image quantification is particularly well suited for the analysis of tissue microarrays which has played a major pivotal role in the rapid assessment of molecular biomarkers. Data acquired from grinding up bulk tissue samples miss spatial information regarding cellular localization; therefore, methods that allow for spatial cell phenotyping at high resolution have proven to be valuable in many biomarker discovery assays. Here, we focus our attention on breast cancer as an example of a tumor type that has benefited from quantitative biomarker studies using tissue microarray format.Areas covered: The history of immunofluorescence and immunohistochemistry and the current status of these techniques, including multiplexing technologies (spectral and non-spectral) and image analysis software will be addressed. Finally, we will turn our attention to studies that have provided proof-of-principle evidence that have been impacted from the use of these techniques.Expert opinion: Assessment of prognostic and predictive biomarkers on tissue sections and TMA using Quantitative immunohistochemistry is an important advancement in the investigation of biologic markers. The challenges in standardization of quantitative technologies for accurate assessment are required for adoption into routine clinical practice.

Association between low estrogen receptor positive breast cancer and staining performance.

Estrogen receptor (ER) expression in breast carcinomas, determined by immunohistochemistry, indicates statistically significant benefit to endocrine therapy in patients with tumors expressing ER in ≥1% of tumor cells. Rare cases with low ER expression (1-10%) lead to the dilemma of treating these tumors as ER positive or negative. We hypothesize that low ER positive result from poor staining performance and that we may detect this artefact by assessing the average dynamic range of normal ducts adjacent to low ER positive tumors. Using quantitative tools, we compare the dynamic range of normal background ER expression in patients with low (1-10%) ER tumors to dynamic range of ER expression in normal epithelium from control patient populations, to determine if low ER cases are accompanied by decreased dynamic range. Low ER cases were infrequent (1% of invasive breast carcinomas). Twenty-one cases with low ER staining and two control cohorts, including a tissue microarray (TMA) of 10 benign breast sections and a group of 34 control breast carcinomas (reported as ER negative or >10% ER positive) with normal background epithelium, were digitally scanned. QuPath was utilized to quantify ER staining for each cell as the mean optical density of nuclear DAB staining. The dynamic range of ER expression in normal epithelium surrounding low ER tumors was significantly lower (range 2-240, median 16.5) than that of the benign epithelium in the control tumors (range 3-475, median 30.8; p < 0.001) and benign TMA sections (range 38-212, median 114; p < 0.001) suggesting inconsistent stainer performance.

Quantitative analysis of CMTM6 expression in tumor microenvironment in metastatic melanoma and association with outcome on immunotherapy.

Chemokine-like factor (CKLF)-like MARVEL transmembrane domain containing 6 (CMTM6) modulates degradation of a number of proteins, including programmed death ligand-1 (PD-L1) by protecting it from ubiquitin-mediated degradation. In this role, it could modulate the effectiveness of immunotherapy. Here, for the first time, we characterize CMTM6 expression in melanoma and evaluate its association with response to immune checkpoint inhibitors (ICI). We evaluated the expression of CMTM6, PD-L1 and other immune-related proteins in 60 pretreatment biopsies from metastatic melanoma patients who received immunotherapy, in a tissue microarray (TMA) using quantitative immunofluorescence (QIF). Expression of mRNA from control patients obtained from The Cancer Genome Atlas (TCGA) database was also compared. CMTM6 expression was positively correlated with PD-L1, CD3, CD20, and CD68 markers, at protein (Pearson's r = 0.53-0.81, all P < .0001) and mRNA (Spearman's r = 0.15-0.44, all P < .002, except for CD68 where P = .26) levels. CMTM6 protein was associated with longer survival after immunotherapy when measured in the stromal (P = .007) and all the immune compartments tested (T cells, B cells, and macrophages). Multivariable analyses also revealed significant CMTM6 survival associations when measured in stromal (Hazard Ratio (HR) = 0.12, P = .001) and CD68-positive (HR = 0.30, P = .043) compartments. Additionally, PD-L1 but not CMTM6 showed prognostic value in control patients. Finally, high CMTM6 and PD-L1 co-expression in the stromal compartment was significantly associated with longer survival in treated patients (P = .028). Consequently, CMTM6 expression shows potential as a predictive factor for ICI treatments.