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.

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.

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.