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 so

NanoCMSer:a consensus molecular subtype stratification tool for fresh-frozen and paraffin-embedded colorectal cancer samples

Colorectal cancer (CRC) is a significant contributor to cancer-related mortality, emphasizing the need for advanced biomarkers to guide treatment. As part of an international consortium, we previously categorized CRCs into four consensus molecular subtypes (CMS1-CMS4), showing promise for outcome prediction. To facilitate clinical integration of CMS classification in settings where formalin-fixed paraffin-embedded (FFPE) samples are routinely used, we developed NanoCMSer, a NanoString-based CMS classifier using 55 genes. NanoCMSer achieved high accuracy rates, with 95% for fresh-frozen samples from the MATCH cohort and 92% for FFPE samples from the CODE cohort, marking the highest reported accuracy for FFPE tissues to date. Additionally, it demonstrated 96% accuracy across a comprehensive collection of 23 RNAseq-based datasets, compiled in this study, surpassing the performance of existing models. Classifying with only 55 genes, the CMS predictions were still biologically relevant, recognizing CMS-specific biology upon enrichment analysis. Additionally, we observed substantial differences in recurrence-free survival curves when comparing CMS2/3 patients in stage III versus II. Probability of recurrence after 5 years increased by 21% in CMS2 and 31% in CMS3 for patients in stage III, whereas this difference was less pronounced for CMS1 and CMS4, with 11% and 10%, respectively. We posit NanoCMSer as a robust tool for subtyping CRCs for both tumor biology and clinical practice, accessible via nanocmser r package (https://github.com/LEXORlab/NanoCMSer) and Shinyapp (https://atorang.shinyapps.io/NanoCMSer).</p

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 (&lt;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.</p

Benefit of adjuvant chemotherapy on recurrence free survival per consensus molecular subtype in stage III colon cancer

The consensus molecular subtype (CMS) classification divides colon tumors into four subtypes holding promise as a predictive biomarker. However, the effect of adjuvant chemotherapy on recurrence free survival (RFS) per CMS in stage III patients remains inadequately explored. With this intention, we selected stage III colon cancer (CC) patients from the MATCH cohort (n = 575) and RadboudUMC (n = 276) diagnosed between 2005 and 2018. Patients treated with and without adjuvant chemotherapy were matched based on tumor location, T- and N-stage (n = 522). Tumor material was available for 464 patients, with successful RNA extraction and CMS subtyping achieved in 390 patients (surgery alone group: 192, adjuvant chemotherapy group: 198). In the overall cohort, CMS4 was associated with poorest prognosis (HR 1.55; p =.03). Multivariate analysis revealed favorable RFS for the adjuvant chemotherapy group in CMS1, CMS2, and CMS4 tumors (HR 0.19; p =.01, HR 0.27; p &lt;.01, HR 0.19; p &lt;.01, respectively), while no significant difference between treatment groups was observed within CMS3 (HR 0.68; p =.51). CMS subtyping in this non-randomized cohort identified patients with poor prognosis and patients who may not benefit significantly from adjuvant chemotherapy.</p

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

Benefit of adjuvant chemotherapy on recurrence free survival per consensus molecular subtype in stage III colon cancer

The consensus molecular subtype (CMS) classification divides colon tumors into four subtypes holding promise as a predictive biomarker. However, the effect of adjuvant chemotherapy on recurrence free survival (RFS) per CMS in stage III patients remains inadequately explored. With this intention, we selected stage III colon cancer (CC) patients from the MATCH cohort (n = 575) and RadboudUMC (n = 276) diagnosed between 2005 and 2018. Patients treated with and without adjuvant chemotherapy were matched based on tumor location, T- and N-stage (n = 522). Tumor material was available for 464 patients, with successful RNA extraction and CMS subtyping achieved in 390 patients (surgery alone group: 192, adjuvant chemotherapy group: 198). In the overall cohort, CMS4 was associated with poorest prognosis (HR 1.55; p =.03). Multivariate analysis revealed favorable RFS for the adjuvant chemotherapy group in CMS1, CMS2, and CMS4 tumors (HR 0.19; p =.01, HR 0.27; p <.01, HR 0.19; p <.01, respectively), while no significant difference between treatment groups was observed within CMS3 (HR 0.68; p =.51). CMS subtyping in this non-randomized cohort identified patients with poor prognosis and patients who may not benefit significantly from adjuvant chemotherapy

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

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 unsupervised feature extraction and selection strategy for identifying epithelial-mesenchymal transition state metrics in breast cancer and melanoma

Digital cytometry is opening up new avenues to better understand the heterogeneous cell types present within the tumor microenvironment. While the focus is towards elucidating immune and stromal cells as clinical correlates, there is still a need to better understand how a change in tumor cell phenotype, such as the epithelial-mesenchymal transition, influences the immune contexture. To complement existing digital cytometry methods, our objective was to develop an unsupervised gene signature capturing a change in differentiation state that is tailored to the specific cellular context of breast cancer and melanoma, as a illustrative example. Towards this aim, we used principal component analysis coupled with resampling to develop unsupervised gene expression-based state metrics specific for the cellular context that characterize the state of cellular differentiation within an epithelial to mesenchymal-like state space and independently correlate with metastatic potential. First developed using cell line data, the orthogonal state metrics were refined to exclude the contributions of normal fibroblasts and to provide tissue-level state estimates based on bulk tissue RNA-seq measures. The resulting gene expression-based 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.</jats:p