Leveraging big data of immune checkpoint blockade response identifies novel potential targets.

BACKGROUND: The development of immune checkpoint blockade (ICB) has changed the way we treat various cancers. While ICB produces durable survival benefits in a number of malignancies, a large proportion of treated patients do not derive clinical benefit. Recent clinical profiling studies have shed light on molecular features and mechanisms that modulate response to ICB. Nevertheless, none of these identified molecular features were investigated in large enough cohorts to be of clinical value. MATERIALS AND METHODS: Literature review was carried out to identify relevant studies including clinical dataset of patients treated with ICB [anti-programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1), anti-cytotoxic T-lymphocyte antigen 4 (CTLA-4) or the combination] and available sequencing data. Tumor mutational burden (TMB) and 37 previously reported gene expression (GE) signatures were computed with respect to the original publication. Biomarker association with ICB response (IR) and survival (progression-free survival/overall survival) was investigated separately within each study and combined together for meta-analysis. RESULTS: We carried out a comparative meta-analysis of genomic and transcriptomic biomarkers of IRs in over 3600 patients across 12 tumor types and implemented an open-source web application (predictIO.ca) for exploration. TMB and 21/37 gene signatures were predictive of IRs across tumor types. We next developed a de novo GE signature (PredictIO) from our pan-cancer analysis and demonstrated its superior predictive value over other biomarkers. To identify novel targets, we computed the T-cell dysfunction score for each gene within PredictIO and their ability to predict dual PD-1/CTLA-4 blockade in mice. Two genes, F2RL1 (encoding protease-activated receptor-2) and RBFOX2 (encoding RNA-binding motif protein 9), were concurrently associated with worse ICB clinical outcomes, T-cell dysfunction in ICB-naive patients and resistance to dual PD-1/CTLA-4 blockade in preclinical models. CONCLUSION: Our study highlights the potential of large-scale meta-analyses in identifying novel biomarkers and potential therapeutic targets for cancer immunotherapy.

Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning.

The extent of tumor-infiltrating lymphocytes (TILs), along with immunomodulatory ligands, tumor-mutational burden and other biomarkers, has been demonstrated to be a marker of response to immune-checkpoint therapy in several cancers. Pathologists have therefore started to devise standardized visual approaches to quantify TILs for therapy prediction. However, despite successful standardization efforts visual TIL estimation is slow, with limited precision and lacks the ability to evaluate more complex properties such as TIL distribution patterns. Therefore, computational image analysis approaches are needed to provide standardized and efficient TIL quantification. Here, we discuss different automated TIL scoring approaches ranging from classical image segmentation, where cell boundaries are identified and the resulting objects classified according to shape properties, to machine learning-based approaches that directly classify cells without segmentation but rely on large amounts of training data. In contrast to conventional machine learning (ML) approaches that are often criticized for their "black-box" characteristics, we also discuss explainable machine learning. Such approaches render ML results interpretable and explain the computational decision-making process through high-resolution heatmaps that highlight TILs and cancer cells and therefore allow for quantification and plausibility checks in biomedical research and diagnostics.

Disruption of the anaphase-promoting complex confers resistance to TTK inhibitors in triple-negative breast cancer.

TTK protein kinase (TTK), also known as Monopolar spindle 1 (MPS1), is a key regulator of the spindle assembly checkpoint (SAC), which functions to maintain genomic integrity. TTK has emerged as a promising therapeutic target in human cancers, including triple-negative breast cancer (TNBC). Several TTK inhibitors (TTKis) are being evaluated in clinical trials, and an understanding of the mechanisms mediating TTKi sensitivity and resistance could inform the successful development of this class of agents. We evaluated the cellular effects of the potent clinical TTKi CFI-402257 in TNBC models. CFI-402257 induced apoptosis and potentiated aneuploidy in TNBC lines by accelerating progression through mitosis and inducing mitotic segregation errors. We used genome-wide CRISPR/Cas9 screens in multiple TNBC cell lines to identify mechanisms of resistance to CFI-402257. Our functional genomic screens identified members of the anaphase-promoting complex/cyclosome (APC/C) complex, which promotes mitotic progression following inactivation of the SAC. Several screen candidates were validated to confer resistance to CFI-402257 and other TTKis using CRISPR/Cas9 and siRNA methods. These findings extend the observation that impairment of the APC/C enables cells to tolerate genomic instability caused by SAC inactivation, and support the notion that a measure of APC/C function could predict the response to TTK inhibition. Indeed, an APC/C gene expression signature is significantly associated with CFI-402257 response in breast and lung adenocarcinoma cell line panels. This expression signature, along with somatic alterations in genes involved in mitotic progression, represent potential biomarkers that could be evaluated in ongoing clinical trials of CFI-402257 or other TTKis.

The Cdc42/Rac1 regulator CdGAP is a novel E-cadherin transcriptional co-repressor with Zeb2 in breast cancer.

The loss of E-cadherin causes dysfunction of the cell-cell junction machinery, which is an initial step in epithelial-to-mesenchymal transition (EMT), facilitating cancer cell invasion and the formation of metastases. A set of transcriptional repressors of E-cadherin (CDH1) gene expression, including Snail1, Snail2 and Zeb2 mediate E-cadherin downregulation in breast cancer. However, the molecular mechanisms underlying the control of E-cadherin expression in breast cancer progression remain largely unknown. Here, by using global gene expression approaches, we uncover a novel function for Cdc42 GTPase-activating protein (CdGAP) in the regulation of expression of genes involved in EMT. We found that CdGAP used its proline-rich domain to form a functional complex with Zeb2 to mediate the repression of E-cadherin expression in ErbB2-transformed breast cancer cells. Conversely, knockdown of CdGAP expression led to a decrease of the transcriptional repressors Snail1 and Zeb2, and this correlated with an increase in E-cadherin levels, restoration of cell-cell junctions, and epithelial-like morphological changes. In vivo, loss of CdGAP in ErbB2-transformed breast cancer cells impaired tumor growth and suppressed metastasis to lungs. Finally, CdGAP was highly expressed in basal-type breast cancer cells, and its strong expression correlated with poor prognosis in breast cancer patients. Together, these data support a previously unknown nuclear function for CdGAP where it cooperates in a GAP-independent manner with transcriptional repressors to function as a critical modulator of breast cancer through repression of E-cadherin transcription. Targeting Zeb2-CdGAP interactions may represent novel therapeutic opportunities for breast cancer treatment.

Genome-wide gene expression profiling to predict resistance to anthracyclines in breast cancer patients.

Validated biomarkers predictive of response/resistance to anthracyclines in breast cancer are currently lacking. The neoadjuvant Trial of Principle (TOP) study, in which patients with estrogen receptor (ER)-negative tumors were treated with anthracycline (epirubicin) monotherapy, was specifically designed to evaluate the predictive value of topoisomerase II-alpha (TOP2A) and develop a gene expression signature to identify those patients who do not benefit from anthracyclines. Here we describe in details the contents and quality controls for the gene expression and clinical data associated with the study published by Desmedt and colleagues in the Journal of Clinical Oncology in 2011 (Desmedt et al., 2011). We also provide R code to easily access the data and perform the quality controls and basic analyses relevant to this dataset.

Modeling invasive breast cancer: growth factors propel progression of HER2-positive premalignant lesions.

The HER2/neu oncogene encodes a receptor-like tyrosine kinase whose overexpression in breast cancer predicts poor prognosis and resistance to conventional therapies. However, the mechanisms underlying aggressiveness of HER2 (human epidermal growth factor receptor 2)-overexpressing tumors remain incompletely understood. Because it assists epidermal growth factor (EGF) and neuregulin receptors, we overexpressed HER2 in MCF10A mammary cells and applied growth factors. HER2-overexpressing cells grown in extracellular matrix formed filled spheroids, which protruded outgrowths upon growth factor stimulation. Our transcriptome analyses imply a two-hit model for invasive growth: HER2-induced proliferation and evasion from anoikis generate filled structures, which are morphologically and transcriptionally analogous to preinvasive patients' lesions. In the second hit, EGF escalates signaling and transcriptional responses leading to invasive growth. Consistent with clinical relevance, a gene expression signature based on the HER2/EGF-activated transcriptional program can predict poorer prognosis of a subgroup of HER2-overexpressing patients. In conclusion, the integration of a three-dimensional cellular model and clinical data attributes progression of HER2-overexpressing lesions to EGF-like growth factors acting in the context of the tumor's microenvironment.

A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?

MOTIVATION: Survival prediction of breast cancer (BC) patients independently of treatment, also known as prognostication, is a complex task since clinically similar breast tumors, in addition to be molecularly heterogeneous, may exhibit different clinical outcomes. In recent years, the analysis of gene expression profiles by means of sophisticated data mining tools emerged as a promising technology to bring additional insights into BC biology and to improve the quality of prognostication. The aim of this work is to assess quantitatively the accuracy of prediction obtained with state-of-the-art data analysis techniques for BC microarray data through an independent and thorough framework. RESULTS: Due to the large number of variables, the reduced amount of samples and the high degree of noise, complex prediction methods are highly exposed to performance degradation despite the use of cross-validation techniques. Our analysis shows that the most complex methods are not significantly better than the simplest one, a univariate model relying on a single proliferation gene. This result suggests that proliferation might be the most relevant biological process for BC prognostication and that the loss of interpretability deriving from the use of overcomplex methods may be not sufficiently counterbalanced by an improvement of the quality of prediction. AVAILABILITY: The comparison study is implemented in an R package called survcomp and is available from http://www.ulb.ac.be/di/map/bhaibeka/software/survcomp/.

Gene regulation by phorbol 12-myristate 13-acetate in MCF-7 and MDA-MB-231, two breast cancer cell lines exhibiting highly different phenotypes.

We have examined the effects of the protein kinase C (PKC)-activator phorbol 12-myristate 13-acetate (PMA) on gene expression in two breast cancer cell (BCC) lines exhibiting highly different phenotypes. These are the estrogen receptor alpha (ERalpha)-positive, weakly invasive, luminal epithelial-like MCF-7 and the ERalpha-negative, highly invasive, fibroblast-like MDA-MB-231. They express constitutively low and high PKC activities, respectively. After a 24-h exposition to 100 nM PMA, the number of genes showing an altered expression at the 2-fold change level was much higher in MCF-7 (n=435) than in MDA-MB-231 (n=18) BCC. Four of these genes, namely CDC2, CENPA, NR4A1 and MMP10, were altered in the same way in both cell lines. Two genes were regulated in an opposite way: ID1 and EVA1. Many of the genes down-regulated in MCF-7 BCC appeared to be preferentially expressed in the G1, S, and/or G2 phases of the cell cycle. The ERalpha gene, ESR1, and other genes associated to the ERalpha-positive, luminal epithelial-like BCC phenotype were down-regulated, while a series of genes related to a more aggressive, fibroblast-like BCC phenotype were up-regulated. Other altered genes were notably linked to cell architecture, supporting profound effects of PMA on cell morphology and motility, as well as on the interactions between BCC and their neighboring proteins. Of note, all the modulated genes involved in proteolysis and its control were up-regulated. In summary, PMA effects suggest that PKC activation may induce, to some extent, a more fibroblast-like phenotype in the ERalpha-positive, luminal epithelial-like MCF-7 BCC, and significantly modulate the interactions of these cells with their environment.