Exploiting a subtype-specific mitochondrial vulnerability for successful treatment of colorectal peritoneal metastases.

Peritoneal metastases (PMs) from colorectal cancer (CRC) respond poorly to treatment and are associated with unfavorable prognosis. For example, the addition of hyperthermic intraperitoneal chemotherapy (HIPEC) to cytoreductive surgery in resectable patients shows limited benefit, and novel treatments are urgently needed. The majority of CRC-PMs represent the CMS4 molecular subtype of CRC, and here we queried the vulnerabilities of this subtype in pharmacogenomic databases to identify novel therapies. This reveals the copper ionophore elesclomol (ES) as highly effective against CRC-PMs. ES exhibits rapid cytotoxicity against CMS4 cells by targeting mitochondria. We find that a markedly reduced mitochondrial content in CMS4 cells explains their vulnerability to ES. ES demonstrates efficacy in preclinical models of PMs, including CRC-PMs and ovarian cancer organoids, mouse models, and a HIPEC rat model of PMs. The above proposes ES as a promising candidate for the local treatment of CRC-PMs, with broader implications for other PM-prone cancers.

Consensus molecular subtype transition during progression of colorectal cancer.

The consensus molecular subtype (CMS) classification divides colorectal cancer (CRC) into four distinct subtypes based on RNA expression profiles. The biological differences between CMSs are already present in CRC precursor lesions, but not all CMSs pose the same risk of malignant transformation. To fully understand the path to malignant transformation and to determine whether CMS is a fixed entity during progression, genomic and transcriptomic data from two regions of the same CRC lesion were compared: the precursor region and the carcinoma region. In total, 24 patients who underwent endoscopic removal of T1-2 CRC were included. Regions were subtyped for CMS and DNA mutation analysis was performed. Additionally, a set of 85 benign adenomas was CMS-subtyped. This analysis revealed that almost all benign adenomas were classified as CMS3 (91.8%). In contrast, CMS2 was the most prevalent subtype in precursor regions (66.7%), followed by CMS3 (29.2%). CMS4 was absent in precursor lesions and originated at the carcinoma stage. Importantly, CMS switching occurred in a substantial number of cases and almost all (six out of seven) CMS3 precursor regions showed a shift to a different subtype in the carcinoma part of the lesion, which in four cases was classified as CMS4. In conclusion, our data indicate that CMS3 is related to a more indolent type of precursor lesion that less likely progresses to CRC and when this occurs, it is often associated with a subtype change that includes the more aggressive mesenchymal CMS4. In contrast, an acquired CMS2 signature appeared to be rather fixed during early CRC development. Combined, our data show that subtype changes occur during progression and that CMS3 switching is related to changes in the genomic background through acquisition of a novel driver mutation (TP53) or selective expansion of a clone, but also occurred independently of such genetic changes. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Elevated temperatures and longer durations improve the efficacy of oxaliplatin- and mitomycin C-based hyperthermic intraperitoneal chemotherapy in a confirmed rat model for peritoneal metastasis of colorectal cancer origin.

Introduction: In patients with limited peritoneal metastasis (PM) originating from colorectal cancer, cytoreductive surgery (CRS) followed by hyperthermic intraperitoneal chemotherapy (HIPEC) is a potentially curative treatment option. This combined treatment modality using HIPEC with mitomycin C (MMC) for 90 minutes proved to be superior to systemic chemotherapy alone, but no benefit of adding HIPEC to CRS alone was shown using oxaliplatin-based HIPEC during 30 minutes. We investigated the impact of treatment temperature and duration as relevant HIPEC parameters for these two chemotherapeutic agents in representative preclinical models. The temperature- and duration- dependent efficacy for both oxaliplatin and MMC was evaluated in an in vitro setting and in a representative animal model. Methods: In 130 WAG/Rij rats, PM were established through i.p. injections of rat CC-531 colon carcinoma cells with a signature similar to the dominant treatment-resistant CMS4 type human colorectal PM. Tumor growth was monitored twice per week using ultrasound, and HIPEC was applied when most tumors were 4-6 mm. A semi-open four-inflow HIPEC setup was used to circulate oxaliplatin or MMC through the peritoneum for 30, 60 or 90 minutes with inflow temperatures of 38°C or 42°C to achieve temperatures in the peritoneum of 37°C or 41°C. Tumors, healthy tissue and blood were collected directly or 48 hours after treatment to assess the platinum uptake, level of apoptosis and proliferation and to determine the healthy tissue toxicity. Results: In vitro results show a temperature- and duration- dependent efficacy for both oxaliplatin and MMC in both CC-531 cells and organoids. Temperature distribution throughout the peritoneum of the rats was stable with normothermic and hyperthermic average temperatures in the peritoneum ranging from 36.95-37.63°C and 40.51-41.37°C, respectively. Treatments resulted in minimal body weight decrease (<10%) and only 7/130 rats did not reach the endpoint of 48 hours after treatment. Conclusions: Both elevated temperatures and longer treatment duration resulted in a higher platinum uptake, significantly increased apoptosis and lower proliferation in PM tumor lesions, without enhanced normal tissue toxicity. Our results demonstrated that oxaliplatin- and MMC-based HIPEC procedures are both temperature- and duration-dependent in an in vivo tumor model.

Subtype-specific kinase dependency regulates growth and metastasis of poor-prognosis mesenchymal colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) can be divided into four consensus molecular subtypes (CMS), each with distinct biological features. CMS4 is associated with epithelial-mesenchymal transition and stromal infiltration (Guinney et al., Nat Med 21:1350-6, 2015; Linnekamp et al., Cell Death Differ 25:616-33, 2018), whereas clinically it is characterized by lower responses to adjuvant therapy, higher incidence of metastatic spreading and hence dismal prognosis (Buikhuisen et al., Oncogenesis 9:66, 2020). METHODS: To understand the biology of the mesenchymal subtype and unveil specific vulnerabilities, a large CRISPR-Cas9 drop-out screen was performed on 14 subtyped CRC cell lines to uncover essential kinases in all CMSs. Dependency of CMS4 cells on p21-activated kinase 2 (PAK2) was validated in independent 2D and 3D in vitro cultures and in vivo models assessing primary and metastatic outgrowth in liver and peritoneum. TIRF microscopy was used to uncover actin cytoskeleton dynamics and focal adhesion localization upon PAK2 loss. Subsequent functional assays were performed to determine altered growth and invasion patterns. RESULTS: PAK2 was identified as a key kinase uniquely required for growth of the mesenchymal subtype CMS4, both in vitro and in vivo. PAK2 plays an important role in cellular attachment and cytoskeletal rearrangements (Coniglio et al., Mol Cell Biol 28:4162-72, 2008; Grebenova et al., Sci Rep 9:17171, 2019). In agreement, deletion or inhibition of PAK2 impaired actin cytoskeleton dynamics in CMS4 cells and, as a consequence, significantly reduced invasive capacity, while it was dispensable for CMS2 cells. Clinical relevance of these findings was supported by the observation that deletion of PAK2 from CMS4 cells prevented metastatic spreading in vivo. Moreover, growth in a model for peritoneal metastasis was hampered when CMS4 tumor cells were deficient for PAK2. CONCLUSION: Our data reveal a unique dependency of mesenchymal CRC and provide a rationale for PAK2 inhibition to target this aggressive subgroup of colorectal cancer.

Development of a miRNA-based classifier for detection of colorectal cancer molecular subtypes.

Previously, colorectal cancer (CRC) has been classified into four distinct molecular subtypes based on transcriptome data. These consensus molecular subtypes (CMSs) have implications for our understanding of tumor heterogeneity and the prognosis of patients. So far, this classification has been based on the use of messenger RNAs (mRNAs), although microRNAs (miRNAs) have also been shown to play a role in tumor heterogeneity and biological differences between CMSs. In contrast to mRNAs, miRNAs have a smaller size and increased stability, facilitating their detection. Therefore, we built a miRNA-based CMS classifier by converting the existing mRNA-based CMS classification using machine learning (training dataset of n = 271). The performance of this miRNA-assigned CMS classifier (CMS-miRaCl) was evaluated in several datasets, achieving an overall accuracy of ~ 0.72 (0.6329-0.7987) in the largest dataset (n = 158). To gain insight into the biological relevance of CMS-miRaCl, we evaluated the most important features in the classifier. We found that miRNAs previously reported to be relevant in microsatellite-instable CRCs or Wnt signaling were important features for CMS-miRaCl. Following further studies to validate its robustness, this miRNA-based alternative might simplify the implementation of CMS classification in clinical workflows.

AKT3 Expression in Mesenchymal Colorectal Cancer Cells Drives Growth and Is Associated with Epithelial-Mesenchymal Transition.

Colorectal cancer (CRC) is a heterogeneous disease that can currently be subdivided into four distinct consensus molecular subtypes (CMS) based on gene expression profiling. The CMS4 subtype is marked by high expression of mesenchymal genes and is associated with a worse overall prognosis compared to other CMSs. Importantly, this subtype responds poorly to the standard therapies currently used to treat CRC. We set out to explore what regulatory signalling networks underlie the CMS4 phenotype of cancer cells, specifically, by analysing which kinases were more highly expressed in this subtype compared to others. We found AKT3 to be expressed in the cancer cell epithelium of CRC specimens, patient derived xenograft (PDX) models and in (primary) cell cultures representing CMS4. Importantly, chemical inhibition or knockout of this gene hampers outgrowth of this subtype, as AKT3 controls expression of the cell cycle regulator p27KIP1. Furthermore, high AKT3 expression was associated with high expression of epithelial-mesenchymal transition (EMT) genes, and this observation could be expanded to cell lines representing other carcinoma types. More importantly, this association allowed for the identification of CRC patients with a high propensity to metastasise and an associated poor prognosis. High AKT3 expression in the tumour epithelial compartment may thus be used as a surrogate marker for EMT and may allow for a selection of CRC patients that could benefit from AKT3-targeted therapy.

Exploring and modelling colon cancer inter-tumour heterogeneity: opportunities and challenges.

Colon cancer inter-tumour heterogeneity is installed on multiple levels, ranging from (epi)genetic driver events to signalling pathway rewiring reflected by differential gene expression patterns. Although the existence of heterogeneity in colon cancer has been recognised for a longer period of time, it is sparingly incorporated as a determining factor in current clinical practice. Here we describe how unsupervised gene expression-based classification efforts, amongst which the consensus molecular subtypes (CMS), can stratify patients in biological subgroups associated with distinct disease outcome and responses to therapy. We will discuss what is needed to extend these subtyping efforts to the clinic and we will argue that preclinical models recapitulate CMS subtypes and can be of vital use to increase our understanding of treatment response and resistance and to discover novel targets for therapy.

An Unsupervised Strategy for Identifying Epithelial-Mesenchymal Transition State Metrics in Breast Cancer and Melanoma.

Digital cytometry aims to identify different cell types in the tumor microenvironment, with the current focus on immune cells. Yet, identifying how changes in tumor cell phenotype, such as the epithelial-mesenchymal transition, influence the immune contexture is emerging as an important question. To extend digital cytometry, we developed an unsupervised feature extraction and selection strategy to capture functional plasticity tailored to breast cancer and melanoma separately. Specifically, principal component analysis coupled with resampling helped develop gene expression-based state metrics that characterize differentiation within an epithelial to mesenchymal-like state space and independently correlate with metastatic potential. First developed using cell lines, the orthogonal state metrics were refined to exclude the contributions of normal fibroblasts and provide tissue-level state estimates using bulk tissue RNA-seq measures. The resulting metrics for differentiation state aim to inform a more holistic view of how the malignant cell phenotype influences the immune contexture within the tumor microenvironment.

An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets.

BACKGROUND: Host immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view. Yet, leveraging transcriptomics data to identify immune cell subtypes presents challenges for extracting informative gene signatures hidden within a high dimensional transcriptomics space characterized by low sample numbers with noisy and missing values. To address these challenges, we explore using machine learning methods to select gene subsets and estimate gene coefficients simultaneously. RESULTS: Elastic-net logistic regression, a type of machine learning, was used to construct separate classifiers for ten different types of immune cell and for five T helper cell subsets. The resulting classifiers were then used to develop gene signatures that best discriminate among immune cell types and T helper cell subsets using RNA-seq datasets. We validated the approach using single-cell RNA-seq (scRNA-seq) datasets, which gave consistent results. In addition, we classified cell types that were previously unannotated. Finally, we benchmarked the proposed gene signatures against other existing gene signatures. CONCLUSIONS: Developed classifiers can be used as priors in predicting the extent and functional orientation of the host immune response in diseases, such as cancer, where transcriptomic profiling of bulk tissue samples and single cells are routinely employed. Information that can provide insight into the mechanistic basis of disease and therapeutic response. The source code and documentation are available through GitHub: https://github.com/KlinkeLab/ImmClass2019 .