Citations

Multiplex immunofluorescence and immunohistochemistry (mIF/IHC) are increasingly employed antibody-based technologies that use tissue sparingly and facilitate the detection of co-localized or neighboring biomarkers. Specifically, these platforms enable spatial analyses of the tumor microenvironment as well as extended applications, for example, describing normal tissue anatomy, autoimmunity, infectious diseases, etc. mIF/IHC has greatly enhanced biomarker discovery efforts, and a growing number of studies suggest superiority to traditional IHC. Standardization of staining approaches, reporting of image analysis strategies and resultant data is critical for facilitating cross-study comparisons, validation, deployment, and generalization of findings. To address this challenge, The Society for Immunotherapy of Cancer (SITC) previously published two articles providing best practice guidelines for mIF/IHC staining, image analysis, and data sharing. Here, SITC convened stakeholders to develop the third article in the series, a consensus checklist for scientific reporting of mIF/IHC data to support and complement the best practice guidelines. The checklist includes critical components of mIF/IHC applications to be defined within publications such as detailed descriptions of analytical validation; image acquisition, selection, and registration methods; and cell clustering and spatial analysis strategies, amongst others. Such information will help with data reproducibility and comparison across studies towards future drug and assay development.

Suppression of ZEB1 through miR-200 expression or HDAC inhibition with mocetinostat sensitizes resistant KRAS mutant lung cancers to MEK inhibition.

Abstract Background: We recently have reported that co-mutations within KRAS-mutated lung adenocarcinoma (LUAD) define a distinct biological phenotype and may offer insights into therapeutic targeting using this classification system. The analysis revealed striking differences in checkpoint ligand expression and T cell infiltration between TP53 and STK11 (LKB1) co-mutated KRAS LUAD. We, therefore, hypothesized that loss of LKB1promotes an immune-suppressed phenotype in KRAS LUAD. To test this, we analyzed 100 immune-related genes from The Cancer Genome Atlas datasets, as well as three other sets of resected tumors from NSLC patients from MD Anderson Cancer Center. Results: Our results revealed that tumors lacking LKB1 (LKB1-loss), as defined by mutation or gene-expression signature, resulted in a down regulation of: tumor associated immunosuppressive ligands, PD-L1, PD-L2, B7-H3, B7-H4, HVEM; T cell associated immunosuppressive ligands PD-1, CTLA-4, BTLA; co-stimulation ligands such as CD80, -86, -40L, and -40. We further examined if this loss of immunosuppression and co-stimulation resulted in changes in markers of T cell phenotypes. Indeed, tumors lacking LKB1-loss tumors demonstrated a distinct lack absence of several T cell markers including CD3, -4, & -8, but also RORC, T-bet, GATA-3, and FoxP3. To determine if myeloid cell populations, known mediators of immune suppression, were also diminished in LKB1-loss tumors, we analyzed expression of CD11c, -103, -11b, -68, and CD33. Consistent with T cell makers and suppressive ligand markers, myeloid markers were down regulated in LKB1-loss tumors. Because common immune suppression markers were lost, we then examined the expression of antigen presentation (HLA-I, HLA-II) and amino acid transporters as possible alternative methods for immune suppression. In LKB1-loss tumors, both HLA-I and HLA-II markers were down regulated, compared to tumors with intact LKB1 expression. Furthermore, genes associated with the antigen presentation complex, LMP2, TAP1&2, and B2M were also down regulated. In contrast to the immune markers described above, cationic transporters (SLC7A2), anionic transporters (SLC7A11), and neutral transporters (SLC3A2) were universally upregulated in LKB1-loss tumors. Conclusions: LKB1 loss is associated with an inert immune phenotype. Our data suggests that reduced antigen presentation and amino acid depletion, but not checkpoint factors or MDSCs, are likely mediator of this phenotype. Citation Format: Warren L. Denning, Ferdinandos Skoulidis, Li Shen, Vassiliki Papadimitrakopoulou, Lixia Diao, Yanyan Lou, Lauren A. Byers, Jing Wang, Jaime R. Canales, Ignacio I. Wistuba, John Weinstein, Don L. Gibbons, John V. Heymach. Loss of LKB1 mediates an immune inert phenotype in human lung adenocarcinoma. [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 4137.

Introduction: The advent of digital slides offers new opportunities within the practice of pathology such as the use of image analysis techniques to facilitate computer aided diagnosis (CAD) solutions. Use of CAD holds promise to enable new levels of decision support and allow for additional layers of quality assurance and consistency in rendered diagnoses. However, the development and testing of prostate cancer CAD solutions requires a ground truth map of the cancer to enable the generation of receiver operator characteristic (ROC) curves. This requires a pathologist to annotate, or paint, each of the malignant glands in prostate cancer with an image editor software - a time consuming and exhaustive process.Recently, two CAD algorithms have been described: probabilistic pairwise Markov models (PPMM) and spatially-invariant vector quantization (SIVQ). Briefly, SIVQ operates as a highly sensitive and specific pattern matching algorithm, making it optimal for the identification of any epithelial morphology, whereas PPMM operates as a highly sensitive detector of malignant perturbations in glandular lumenal architecture.Methods: By recapitulating algorithmically how a pathologist reviews prostate tissue sections, we created an algorithmic cascade of PPMM and SIVQ algorithms as previously described by Doyle el al. [1] where PPMM identifies the glands with abnormal lumenal architecture, and this area is then screened by SIVQ to identify the epithelium.Results: The performance of this algorithm cascade was assessed qualitatively (with the use of heatmaps) and quantitatively (with the use of ROC curves) and demonstrates greater performance in the identification of malignant prostatic epithelium.Conclusion: This ability to semi-autonomously paint nearly all the malignant epithelium of prostate cancer has immediate applications to future prostate cancer CAD development as a validated ground truth generator. In addition, such an approach has potential applications as a pre-screening/quality assurance tool.