Abstract P1-02-17: Artificial intelligence-based whole slide scoring of nuclear breast cancer IHC markers Ki67, ER, and PR matches performance of manual clinical scoring

Background: 2.1 million breast cancers are newly diagnosed each year. Current guidelines endorse routine testing for estrogen receptor (ER) and progesterone receptor (PR), while use of the Ki67 biomarker can provide additional prognostic value. All three biomarkers currently require quantitative evaluation using manual review of a glass slide, resulting in reproducibility issues across labs due to interpretative and scoring variabilities. Current on-market image analysis algorithms only offer limited field-of-view (FOV) that analyze a tiny fraction of the entire tissue. Whole slide image (WSI) analysis, in comparison, analyzes the entire tissue and, therefore, more closely mimics how pathologists assess these slides in clinical practice. In this work, we developed three deep learning artificial intelligence (AI) based algorithms for WSI analysis (IA) of digitized images from Ki67, ER, and PR stained slides that address these variabilities and allow pathologists across labs to consistently score at the same accuracy as selected expert labs. The complete software solution delivers high throughput analyzing whole-slide images in less than 2 minutes during pre-computation on conventional computer hardware and returning results on user-provided annotations in milliseconds. Methods: We assembled a benchmark validation data set of 312 breast cancer cases (100 Ki67, 102 ER, and 110 PR slides, stained at multiple sites) representative of breast cancer subtypes (i.e. ductal, lobular, mucinous, medullary, tubular), score (i.e. 0%-100% positivity), tumor grade (i.e. well, moderately, and poorly differentiated), and specimen type (i.e. biopsy and resection). Three pre-clinical validation studies were performed using the Roche uPath enterprise software and each of the ER, PR and Ki67 image analysis algorithms. A total of 6 pathologists participated in the study split into expert (n=3) and study (n=3) readers. A non-inferiority Ground-Truth (GT) study design was implemented in which the study and expert readers performed manual read (MR) followed by AI-assisted scoring. The expert manual scores were used as GT to which the readers’ manual and AI scores were compared for each marker and case. Results: The overall concordance rates between AI scores and expert GT was as follows: For Ki67, OPA=97.2% (95% CI: 94.0, 99.7), NPA=97.8% (95% CI: 93.4,100), and PPA=96.7% (95% CI: 91.3, 100), for ER, OPA=95.4% (CI:91.4,98.4), NPA=96.4% (CI:92.5,99.4), and PPA=94.4% (CI:87.4,100), and for PR, OPA=96.1% (95% CI:92.7,99.1), NPA=96.7% (95% CI:92.5,100), and PPA=95.6% (95% CI:89.9,100). The differences between AI and MR overall concordance rates (AI-MR) when compared to the expert GT were: for Ki67: OPA-diff=1.4% (2-sided 95% CI:-0.7,3.7), NPA-diff=3.8% (CI:0.6,7.8), PPA-diff=-1.0% (CI:-3.5,0.0), for ER: OPA-diff=-0.9% (CI:-3.3,1.0), NPA-diff=-0.1% (CI:-3.1,3.0), PPA-diff=-1.8% (CI:-6.2,0.0), and for PR: OPA-diff=-1.5% (CI:-3.9,0.6), NPA-diff=-2.4% (CI:-6.8,1.2), PPA-diff= -0.7%(CI:-2.8,1.1) using the cutoffs 20% (Ki67), 1% (ER), and 1% (PR) respectively. Conclusion: Our preliminary feasibility data shows that pathologists using WSI analysis assisted scoring was equivalent to manual scoring and an expert panel GT using a truly representative benchmark data set. Additionally, image analysis algorithms are known to provide high reproducibility and precision. We will provide those numbers at a later stage as they were not fully available at time of submission. Our results show the value and potential of deep learning technologies to improve the diagnosis and care of patients with breast cancer.

Citation Format: Monesh Kapadia, Mehrnoush Khojasteh, Margarita Kouzova, Carol Jones, Xiao-Meng Xu, Matthew T. Olson, Sarah Gladden, Nancy Sapanara, Shalini Singh, Chen Chun Chen, Isaac Bai, Jim Ranger-Moore, Landon J. Inge, Uday Kurkure, Ipshita Bhattacharya, Margaret Zhao, Karel Zuiderveld, Chandana Chintakindi, Bryan Lopez, Christoph Guetter. Artificial intelligence-based whole slide scoring of nuclear breast cancer IHC markers Ki67, ER, and PR matches performance of manual clinical scoring [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-02-17.

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.

Abstract 4030: Case classification with tumor antigen presenting and TGF-β signaling biomarkers to predict anti-PD-1 outcome in GI tract tumors using automated quantitative fluorescence multiplex IHC

Introduction: Anti-PD-1/L1 immune checkpoint blockade results in tumor stabilization or shrinkage in only 15-40% of patients. Predictive biomarkers are crucial in identifying responsive patients while excluding others from the toxicities of immunotherapies. Major histocompatibility complex class I (MHC-I) downregulation is one of the most frequent mechanisms of tumor escape from the host’s immune system, but little attention has been devoted to MHC-I expression in studies of the PD-1/L1 blockade. Recently, Tauriello and Mariathasan revealed stromal transforming growth factor (TGF)-β signaling in CD8+ T lymphocytes exclusion as a key determinant of resistance to PD-1/L1 blockade in colorectal and urothelial carcinomas. We present here a multiplex panel IHC of MHC-I, β2-microglobulin (B2M), CD14, TGF-β receptor 2 (TGFBR2), and pan-cytokeratin (panCK) in tumor micro-environment, and their predictive values to anti-PD-1 treatment.

Methods: With the multiplex panel, 51 pre-pembrolizumab treatment patient specimens were stained, including pancreatic, colorectal and cholangiocarcinoma (33 non-responders: 17 PD, 13 SD, 3 NE; 18 responders: 14 PR, 4 CR). Pathologists annotated tumor areas on whole slide scans. HALO High-Plex FL module was used for image analysis. Epithelial tumor (panCK+) and stroma (panCK-) were masked with HALO’s random forest classifier. Spatial location, count, intensity, and percent abundance of each marker were identified. 43 features were designed based on the rationale of hypothesized biological significance. MATLAB was used for feature selection, ranking, and prediction of responses to anti-PD1 treatment.

Results: There was a trend of higher MHCI expression on tumor cells in the responders than non-responders to pembrolizumab treatment. Heterogeneous MHCI expression of tumor cells, and fraction of TGFBR2+ CD14+ cells in stroma were the top features ranked by Relieff k-nearest neighbor (k=30) for the prediction of the response to pembrolizumab treatment. Using Quadratic Discriminant Analysis (QDA) with five-fold cross-validation, the prediction accuracy was 76.5%. Independent validation was not performed due to small sample size.

Conclusions: Deep immune characterization of tumor microenvironments using high dimensional feature spaces derived from multiplex IHC staining may provide insightful directions on finding and validating predictive markers for various immunotherapy regiments (ex. PD-1/L1 blockade; dual TGF-β and PD-1/L1 blockade; combination of PD-1/L1 blockade with other treatments that enhance MHC-I molecules on tumor cells).

Acknowledgement: We thank Nick Cummins, Jorge Lozano, and John Hurley for their technical assistance.

Citation Format: Xiangxue Wang, Shizen Moh, Antony Hubbard, José L. Muñoz-Rodríguez, Mehrnoush Khojasteh, Jim Martin, Qingfeng Zhu, Robert Anders, Luis Diaz, Lidija Pestic-Dragovich, Lei Tang, Wenjun Zhang. Case classification with tumor antigen presenting and TGF-β signaling biomarkers to predict anti-PD-1 outcome in GI tract tumors using automated quantitative fluorescence multiplex IHC [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 4030.

Abstract 5117: An automated 5-plex fluorescent immunohistochemistry enabled characterization of PD-L1 expression and tumor infiltrating immune cells in lung and bladder cancer specimens

Cancers may escape immune surveillance and eradication through the expression of programmed death-ligand 1 (PD-L1) on tumor cells and in the tumor microenvironment. PD-L1 expression has been reported in various cell populations within the tumor, and its expression associated with prognosis for various tumors. Further, clinical studies have shown that this pathway is an important target for immunotherapy and PD-L1 expression on tumor cells and in the tumor microenvironment has been associated with enhanced response. Understanding PD-L1's complex biological function not only on tumor cells but also within the tumor microenvironment requires simultaneous interrogation of multiple biomarkers, ranging from cancer immunology checkpoint markers, tumor infiltrating immune cell markers, and tumor specific markers, etc. Multiplex immunohistochemistry (IHC) allows simultaneous detection of multiple markers to explore the potential cellular composition of immune/stromal/cancer cells in tumor microenvironment. Development of a multiplex IHC assay remains challenging due to antibody species similarity and cross reactivity, stability of fluorophores through multiple rounds of processing, balancing high and low signals and measurement of weakly expressed markers. We present here the development of a fully automated multiplex IHC assay (PD-L1, CD3, CD8, CD68 and FoxP3) using rabbit primary antibodies with a heat deactivation process between each antigen staining cycles on the BenchMark ULTRA automated slide stainer. As part of the technology validation, we compared the 5-plex IHC to the respective single-plex chromogenic IHC assays. Using the automated 5-plex fluorescent IHC assay, we tested a cohort of non-small cell lung (NSCLC) and bladder cancer tissue specimens and characterized PD-L1 and immune marker expression in both tumor and infiltrate immune cells. To provide an objective and reliable readout of the assay, image analysis tools are being developed for automated identification and quantification of the labelled biomarkers and their co-expression on a cell-by-cell basis. This automated multiplex PD-L1 5-Plex IHC assay could be utilized as a tool for further characterizing tumors and its microenvironment and gain a better understanding of which patients may benefit from immune-therapies.

Citation Format: Wenjun Zhang, Antony Hubbard, Adriana Racolta, Nick Cummins, Mehrnoush Khojasteh, Liping Zhang, Karl Garsha, Joerg Bredno, Dustin Harshman, Srabani Bhaumik, Tobin Jones, Marcin Kowanetz, Sanjeev Mariathasan, Ian McCaffery, Dustin Smith, J Andrew Williams, Lidija Pestic-Dragovich, Larry Morrison, Lei Tang. An automated 5-plex fluorescent immunohistochemistry enabled characterization of PD-L1 expression and tumor infiltrating immune cells in lung and bladder cancer specimens. [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 5117.

A framework for quantitative assessment of Ki67 distribution in preneoplastic bronchial epithelial lesions

OBJECTIVE

Deregulated cell proliferation is a hallmark of cancer, and Ki67 immunostaining can be used to identify proliferating cells. Evaluation of cell proliferation may have utility as a biomarker of epithelial malignant transformation risk. To date, most analyses of Ki67 staining have been restricted to semiquantitative estimations of the degree of staining or the measurement of the fraction of Ki67-positive cells within the epithelium. We sought to develop a robust, objective means of quantitatively evaluating Ki67 immunostaining for lung precancerous lesions.

STUDY DESIGN

We quantified the spatial distribution of Ki67-expressing cells within the epithelium by means of (1) a cell-based Voronoi tessellation and (2) a basement membrane-referenced distance transform. This was undertaken in a large cohort of 613 lung biopsy sections representing normal, hyperplasia, squamous metaplasia and mild, moderate and severe dysplasia. For each section 21 features quantifying different aspects of the Ki67 staining were calculated. Intraobserver and inter-observer variation were recorded for a subset of the biopsy sections. We examined the behavior of each feature with respect to histopathological grade.

RESULTS

These measures demonstrated that proliferation is generally limited to layers 2, 3 and 4 of the epithelium (layer 1 being the basal layer). The proliferation in the basal layer is limited and does not increase with increasing grade of dysplasia. Interobserver and intraobserver effects on these features were assessed, and several were more robust with respect to measuring Ki67 expression pattern than the commonly used fraction of Ki67-positive cells.

CONCLUSION

Many of these quantitative features showed associations with histological grade that were as strong as the association that exists based on the fraction of Ki67-positive cells while being much more robust to interobserver- and intraobserver-associated variations. The measured spatial distribution of proliferating cells statistically demonstrated asymmetric cell division behavior in cells in the basal layer, a pattern attributed to stem cells giving rise to transient amplifying cells.

Abstract A18: Spatial dynamics of cellular proliferation in preneoplastic lesions: Comparison between bronchial, oral, and cervical epithelium

Introduction: Uncontrolled proliferation is a hallmark of cancer and a biologically plausible risk biomarker for preneoplastic epithelium. The expression Ki-67 has been widely used as a marker of cellular proliferation even though it does not specifically elucidate the complexity of clonal growth that eventually drives a normal homeostatic epithelium towards carcinoma in situ. Our objective is to quantify the spatial dynamics of cellular proliferation in pre-neoplastic lesions of three different tissue types; bronchial, oral and cervical epithelium.

Materials and Methods: Five hundred sixty-two bronchial lesions, seventy nine oral mucosa lesions, and two hundred sixty five cervical lesions were reviewed by at least two pathologists. Expression of Ki-67 was studied by Mib-1 immunohistochemistry. Using an in-house imaging system, a mapping of the regions of interest were performed; nuclei positions were registered; Mib-1 positive cells were manually marked, the basal membrane and the external surface delineated. Using graph-theory tools, the spatial arrangements of all nuclei, in addition to the spatial distribution of Mib-1 positive nuclei were measured. The percentage of Mib-1 positive nuclei within each layer - from the basal layer to the superficial layer - was a study focus.

Results: On average, proliferation increased with pathology grade. Nevertheless, the amplitude and the patterns of cellular proliferation within the different layers differ among the different tissue types. The propotions of Mib-1 positive cells in the bronchial, oral, and cervical normal epithelium were respectively: 19.8%, 6.3%, and 6.1% in the basal layer; 18.7%, 28.5% and 33.2 in the layer 1; 8.0%, 18.7% and 31.1% in the layer 2. For specimens classified as mild dysplasia, the proportion of Mib-1 positive cells in bronchial, oral and cervical dysplastic epithelium were respectively: 42.0%, 19.3%, and 21.5% in the basal layer; 63,2%, 19.3%, and 43.5% in layer 1; 61.8%, 9.9% and 50.0% in layer 2. Furthermore, we observed a high variability of the proliferation patterns (along the different layers) in each tissue within each pathology grade. The significance of these findings and their correlation with other biomarkers (quantitative nuclear phenotype, p16 staining, LOH, etc.) and with clinical parameters (age, sex, HPV status, cancer progression, etc..) will be shown.

Conclusions: Quantitative analysis of spatial patterns and arrangement of proliferating cells squamous dysplastic lesions provide additional insights in the dynamic nature of these early neoplastic changes.

Citation Information: Cancer Prev Res 2010;3(1 Suppl):A18.

Abstract A28: Improving detection through SELF (selective excitation light fluorescence) imaging

The use of fluorescence imaging and spectroscopy for the detection of early neoplastic epithelial lesions has been well established, with clinically adopted devices in for use in the lung (Onco-LIFE, SAFE-1000, etc), the oral cavity (VELScope and Identaf 3000) and others in development for the cervix and dermatology. Primarily these imaging technologies make use of fluorescence excitation illumination of a single wavelength (predominately in the near UV to blue wavelength ranges) and differentiate between normal and not normal tissue based upon intensity changes and spectral shifts in the emitted tissue autofluorescence (not normal -darker with less green or blue relative the red fluorescence).

SELF imaging makes use of a multitude of illumination wavelengths to specifically couple to the action spectra of the fluorophores within the tissue to enhance the differentiation between tissue states, fluorophores and their immediate environment. This methodology makes use of the different absorption spectra (action spectra) of different fluorophores or similar flourophores in different environments. Conventionally this can be done through the sequential illumination with many different excitation wavelengths and sequential image capture, to collect a hyperspectral excitation image data cube followed by some form of spectra unmixing to resolve the individual targets (components) contributing to the image. Each target is identified by a unique weighted sum of pixel intensities across the excitation wavelengths in the data cube.

Through the use of a programmable light source such as the OneLight (OneLight Corp.) in which not only the wavelengths of the illumination light, but their individual intensities (alone or in combination) can be selected under computer control it is possible to not only rapidly illuminate with separate excitation wavelengths but to illuminate with a collection of weighted (each wavelength has a different selected intensity) spectra to specifically couple to selected fluorescence targets (specific fluorophores or fluorophores in specific local environments). Thus instead of needing to illuminate with a series of 10 separate excitation wavelengths and collect separate 10 images on can illuminate with a few (2–3) weighted profiles of excitation wavelengths and collect a few (2–3) images, the number of spectra used (images collected) determines the number of targets differentiated. In this fashion it is possible to detect in an image many more specific flourophore types than with conventional fluorescence imaging. SELF imaging in microscopy, wide field macroscopic imaging and ex vivo and in vivo imaging has been demonstrated and will be presented.

Citation Information: Cancer Prev Res 2010;3(1 Suppl):A28.

Integrating copy number polymorphisms into array CGH analysis using a robust HMM

MOTIVATION

Array comparative genomic hybridization (aCGH) is a pervasive technique used to identify chromosomal aberrations in human diseases, including cancer. Aberrations are defined as regions of increased or decreased DNA copy number, relative to a normal sample. Accurately identifying the locations of these aberrations has many important medical applications. Unfortunately, the observed copy number changes are often corrupted by various sources of noise, making the boundaries hard to detect. One popular current technique uses hidden Markov models (HMMs) to divide the signal into regions of constant copy number called segments; a subsequent classification phase labels each segment as a gain, a loss or neutral. Unfortunately, standard HMMs are sensitive to outliers, causing over-segmentation, where segments erroneously span very short regions.

RESULTS

We propose a simple modification that makes the HMM robust to such outliers. More importantly, this modification allows us to exploit prior knowledge about the likely location of "outliers", which are often due to copy number polymorphisms (CNPs). By "explaining away" these outliers with prior knowledge about the locations of CNPs, we can focus attention on the more clinically relevant aberrated regions. We show significant improvements over the current state of the art technique (DNAcopy with MergeLevels) on previously published data from mantle cell lymphoma cell lines, and on published benchmark synthetic data augmented with outliers.

AVAILABILITY

Source code written in Matlab is available from http://www.cs.ubc.ca/~sshah/acgh.

A stepwise framework for the normalization of array CGH data

BACKGROUND

In two-channel competitive genomic hybridization microarray experiments, the ratio of the two fluorescent signal intensities at each spot on the microarray is commonly used to infer the relative amounts of the test and reference sample DNA levels. This ratio may be influenced by systematic measurement effects from non-biological sources that can introduce biases in the estimated ratios. These biases should be removed before drawing conclusions about the relative levels of DNA. The performance of existing gene expression microarray normalization strategies has not been evaluated for removing systematic biases encountered in array-based comparative genomic hybridization (CGH), which aims to detect single copy gains and losses typically in samples with heterogeneous cell populations resulting in only slight shifts in signal ratios. The purpose of this work is to establish a framework for correcting the systematic sources of variation in high density CGH array images, while maintaining the true biological variations.

RESULTS

After an investigation of the systematic variations in the data from two array CGH platforms, SMRT (Sub Mega base Resolution Tiling) BAC arrays and cDNA arrays of Pollack et al., we have developed a stepwise normalization framework integrating novel and existing normalization methods in order to reduce intensity, spatial, plate and background biases. We used stringent measures to quantify the performance of this stepwise normalization using data derived from 5 sets of experiments representing self-self hybridizations, replicated experiments, detection of single copy changes, array CGH experiments which mimic cell population heterogeneity, and array CGH experiments simulating different levels of gene amplifications and deletions. Our results demonstrate that the three-step normalization procedure provides significant improvement in the sensitivity of detection of single copy changes compared to conventional single step normalization approaches in both SMRT BAC array and cDNA array platforms.

CONCLUSION

The proposed stepwise normalization framework preserves the minute copy number changes while removing the observed systematic biases.