CD8 IHC repeated measures staining results for 25 breast (red) and colorectal (blue) carcinoma samples.

<p>Each tumor block was sectioned 8 times or until the sample was exhausted.</p

CD8+ T cell infiltration in breast and colon cancer: A histologic and statistical analysis

<div><p>The prevalence of cytotoxic tumor infiltrating lymphocytes (TILs) has demonstrated prognostic value in multiple tumor types. In particular, CD8 counts (in combination with CD3 and CD45RO) have been shown to be superior to traditional UICC staging in colon cancer patients and higher total CD8 counts have been associated with better survival in breast cancer patients. However, immune infiltrate heterogeneity can lead to potentially significant misrepresentations of marker prevalence in routine histologic sections. We examined step sections of breast and colorectal cancer samples for CD8+ T cell prevalence by standard chromogenic immunohistochemistry to determine marker variability and inform practice of T cell biomarker assessment in formalin-fixed, paraffin-embedded (FFPE) tissue samples. Stained sections were digitally imaged and CD8+ lymphocytes within defined regions of interest (ROI) including the tumor and surrounding stroma were enumerated. Statistical analyses of CD8+ cell count variability using a linear model/ANOVA framework between patients as well as between levels within a patient sample were performed. Our results show that CD8+ T-cell distribution is highly homogeneous within a standard tissue sample in both colorectal and breast carcinomas. As such, cytotoxic T cell prevalence by immunohistochemistry on a single level or even from a subsample of biopsy fragments taken from that level can be considered representative of cytotoxic T cell infiltration for the entire tumor section within the block. These findings support the technical validity of biomarker strategies relying on CD8 immunohistochemistry.</p></div

Block-level biopsy simulation results.

<p>(A) Percent of times over 1000 rounds of simulations that the values obtained from sampling increased numbers of biopsy fragments produced a result within 1SD of the mean CD8 staining for an entire tumor block. Calculating the mean over increased numbers of fragments led to better estimates of the mean, while calculating maxima over the sample biopsies led to overestimates of a sample’s CD8 levels. Samples are sorted by increasing performance in terms of being able to produce an estimate within 1SD of total CD8 staining for that tumor block. (B) Estimates of the standard deviation of the difference between the mean or maximum of selected core biopsies and the mean or maximum of the out-of-bag or unselected cores for a given tumor sample. Increased sampling leads to improvements in variability for when using means but not when using order statistics. (C) Estimates of the standard deviation of the difference between the mean or maximum of selected core biopsies and the mean or maximum of the out-of-bag or unselected cores for a given tumor sample as a function of the mean CD8 percent positive staining for that block. Loess fits are used to highlight mean performance over the observed dynamic range on both the log₂ and observed percent staining scales. Increased sampling leads to improvements in variability when using means but not when using order statistics.</p

Sample workflow for immunohistochemistry and image analysis.

<p>Tumor blocks were sectioned and levels for CD8 immunohistochemistry taken at 25 μm intervals. Stained slides were scanned and the tumor area and immediately adjacent stroma was manually designated by a pathologist on all slides. All nucleated cells as well as CD8+ cells within the defined area were identified and counted by image analysis. Simulated core biopsies were identified by creating a grid of rectangular regions over the entire image, each approx. 2mm². in size. Rectangular regions that overlapped with at least 0.7 mm² of manually identified region were analyzed. Scale bar illustrated in “Level 1” panel equals 500 μm.</p

Slide-level biopsy simulation results.

<p>(A) Percent of times over 1000 rounds of simulations that the values obtained from sampling increased numbers of biopsy fragments produced a result within 1SD of the mean CD8 staining for a given slide. Calculating the mean over increased numbers of biopsies led to better estimates of the mean, while calculating maxima over the sample biopsies led to overestimates of a slide’s CD8 levels. Samples are sorted by increasing performance in terms of being able to produce an estimate within 1SD of total CD8 staining for that slide. (B) Estimates of the standard deviation of the difference between the mean or maximum of selected core biopsies and the mean or maximum of the out-of-bag or unselected cores on a given slide. Increased sampling leads to improvements in variability for when using means but not when using order statistics. (C) Estimates of the standard deviation of the difference between the mean or maximum of selected core biopsies and the mean or maximum of the out-of-bag or unselected cores on a given slide as a function of the mean CD8 percent positive staining for a slide. Loess fits are used to highlight mean performance over the observed dynamic range on both the log₂ and observed percent staining scales. Increased sampling leads to improvements in variability when using means but not when using order statistics.</p

Simulation receiver-operator characteristic (ROC) curves.

<p>With increased sampling, there is increased performance of the CD8 IHC assay to classify as positive or negative for a given cut-off (different colors) when using the means of different numbers of sampled biopsies (left) and decreased performance when using maxima (right). Over the set of cut-offs (1%, 2%, 5%, 10%), the analysis treated the mean CD8 staining as the true intensity against which the results of the subsamples of biopsy fragments were evaluated. Similar results were obtained when benchmarking the staining result from core biopsies against the mean staining of the entire block (data not shown).</p

First-In-Human Phase I Study of the OX40 Agonist MOXR0916 in Patients with Advanced Solid Tumors.

PURPOSE: OX40, a receptor transiently expressed by T cells upon antigen recognition, is associated with costimulation of effector T cells and impairment of regulatory T-cell function. This first-in-human study evaluated MOXR0916, a humanized effector-competent agonist IgG1 monoclonal anti-OX40 antibody. PATIENTS AND METHODS: Eligible patients with locally advanced or metastatic refractory solid tumors were treated with MOXR0916 intravenously once every 3 weeks (Q3W). A 3+3 dose-escalation stage (0.2-1,200 mg; n = 34) was followed by expansion cohorts at 300 mg (n = 138) for patients with melanoma, renal cell carcinoma, non-small cell lung carcinoma, urothelial carcinoma, and triple-negative breast cancer. RESULTS: MOXR0916 was well tolerated with no dose-limiting toxicities observed. An MTD was not reached. Most patients (95%) experienced at least one adverse event (AE); 56% of AEs, mostly grade 1-2, were related to MOXR0916. Most common treatment-related AEs included fatigue (17%), diarrhea (8%), myalgia (7%), nausea (6%), decreased appetite (6%), and infusion-related reaction (5%). Pharmacokinetic (PK) parameters were dose proportional between 80 and 1,200 mg and supported Q3W administration. The recommended expansion dose based on PK and OX40 receptor saturation was 300 mg Q3W. Immune activation and upregulation of PD-L1 was observed in a subset of paired tumor biopsies. One renal cell carcinoma patient experienced a confirmed partial response. Overall, 33% of patients achieved stable disease. CONCLUSIONS: Although objective responses were rarely observed with MOXR0916 monotherapy, the favorable safety profile and evidence of tumor immune activation in a subset of patients support further investigation in combination with complementary agents such as PD-1/PD-L1 antagonists

Molecular determinants of response to PD-L1 blockade across tumor types.

Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis lead to durable clinical responses in subsets of cancer patients across multiple indications, including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC) and renal cell carcinoma (RCC). Herein, we complement PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) with RNA-seq in 366 patients to identify unifying and indication-specific molecular profiles that can predict response to checkpoint blockade across these tumor types. Multiple machine learning approaches failed to identify a baseline transcriptional signature highly predictive of response across these indications. Signatures described previously for immune checkpoint inhibitors also failed to validate. At the pathway level, significant heterogeneity is observed between indications, in particular within the PD-L1+ tumors. mUC and NSCLC are molecularly aligned, with cell cycle and DNA damage repair genes associated with response in PD-L1- tumors. At the gene level, the CDK4/6 inhibitor CDKN2A is identified as a significant transcriptional correlate of response, highlighting the association of non-immune pathways to the outcome of checkpoint blockade. This cross-indication analysis reveals molecular heterogeneity between mUC, NSCLC and RCC tumors, suggesting that indication-specific molecular approaches should be prioritized to formulate treatment strategies