The Multidisciplinary Management of Cutaneous Squamous Cell Carcinoma: A Comprehensive Review and Clinical Recommendations by a Panel of Experts

Cutaneous squamous cell carcinomas (CSCC) account for about 20% of all keratinocyte carcinomas, which are the most common form of cancer. Heterogeneity of treatments and low mortality are a challenge in obtaining accurate incidence data and consistent registration in cancer registries. Indeed, CSCC mostly presents as an indolent, low-risk lesion, with five-year cure rates greater than 90% after surgical excision, and only few tumors are associated with a high-risk of local or distant relapse; therefore, it is particularly relevant to identify high-risk lesions among all other low-risk CSCCs for the proper diagnostic and therapeutic management. Chemotherapy achieves mostly short-lived responses that do not lead to a curative effect and are associated with severe toxicities. Due to an etiopathogenesis largely relying on chronic UV radiation exposure, CSCC is among the tumors with the highest rate of somatic mutations, which are associated with increased response rates to immunotherapy. Thanks to such strong pre-clinical rationale, clinical trials led to the approval of anti-PD-1 cemiplimab by the FDA (Food and Drug Administration) and EMA (European Medicines Agency), and anti-PD-1 pembrolizumab by the FDA only. Here, we provide a literature review and clinical recommendations by a panel of experts regarding the diagnosis, treatment, and follow-up of CSCC.

Effects of Multi-Shell Free Water Correction on Glioma Characterization

Diffusion MRI is a useful tool to investigate the microstructure of brain tumors. However, the presence of fast diffusing isotropic signals originating from non-restricted edematous fluids, within and surrounding tumors, may obscure estimation of the underlying tissue characteristics, complicating the radiological interpretation and quantitative evaluation of diffusion MRI. A multi-shell regularized free water (FW) elimination model was therefore applied to separate free water from tissue-related diffusion components from the diffusion MRI of 26 treatment-naïve glioma patients. We then investigated the diagnostic value of the derived measures of FW maps as well as FW-corrected tensor-derived maps of fractional anisotropy (FA). Presumed necrotic tumor regions display greater mean and variance of FW content than other parts of the tumor. On average, the area under the receiver operating characteristic (ROC) for the classification of necrotic and enhancing tumor volumes increased by 5% in corrected data compared to non-corrected data. FW elimination shifts the FA distribution in non-enhancing tumor parts toward higher values and significantly increases its entropy (p ≤ 0.003), whereas skewness is decreased (p ≤ 0.004). Kurtosis is significantly decreased (p < 0.001) in high-grade tumors. In conclusion, eliminating FW contributions improved quantitative estimations of FA, which helps to disentangle the cancer heterogeneity.

Differential Gene Expression and Functional Enrichment Analysis of Smoothed PDAC Spatial Transcriptomics Slides

Background: Cancer heterogeneity can impact diagnosis and therapeutics. This has been studied at the cellular level using single cell RNA sequencing (scRNA-seq) but not spatially. Spatial transcriptomics (ST) has recently enabled measuring transcriptomes at hundreds of locations in a tissue, revolutionizing our understanding of tumor heterogeneity. Low RNA quantities in scRNA-seq and ST introduce missed reads and noise. Multiple methods have been developed to impute and smooth scRNA-seq data including MAGIC and SAVER. To date, only our newly developed spatial and pattern combined smoothing (SPCS) method has been developed specifically for ST. We compared the biological interpretability of these smoothing methods to determine which method best informs tumor heterogeneity. Methods: Ten ST slides from six patients with PDAC were computationally smoothed using SAVER, MAGIC, and SPCS. ST spots from unsmoothed and smoothed slide were split into TM4SF1 high or low expression groups and differentially expressed genes (DEG) found. Significant up- and down-regulated DEGs were used for functional enrichment analysis (EA) to compare the biological insights each method provides. Results: DEGs were found in eight samples, with SPCS finding the most DEGs in six of the eight samples. The number of EA terms generally correlated with the numbers DEGs. SPCS had the most up-regulated enriched terms for every ST slide. Top ten up- and down-regulated EA terms were presented from one ST slide to demonstrate that SPCS gives more biologically interpretable results. Lastly, we present SPCS terms previously been reported in PDAC or involved in the TM4SF1 cancer pathway. Conclusion: Smoothing ST data using SPCS provided more DEGs and more useful EA terms. The EA terms found using SPCS are more consistent with our current understanding of PDAC. Not smoothing, MAGIC, and SAVER missed many important terms which SPCS found. Compared to other methods, SPCS smoothed data is more interpretable.

Patient-Specific Tumor Microenvironment Models

The heterogeneity of cancer makes it difficult to predict the prognosis of treatment. There is still a lack of preclinical model systems that reflect the clinical characteristics of patients who have heterogenetic tumors. Advances in 3-dimentional (3D) cell culture are leading to discoveries that occur in the development and progression of cancer that has not been known. There are many models including patient-derived xenograft, patient-derived organoid and spheroid, patient-derived explant, scaffold-based model, and system-based model. Each 3D model has its strengths and limitations. One model cannot answer every question, so it seems most reasonable to approach multiple models when studying cancer heterogeneity. Hopefully, 3D tumor modeling will make tremendous progress on this path by fusion of innovative biomaterials and advanced modeling techniques that can partially mimic the heterogeneous environment of real tumors.

Epigenetic Therapeutic Strategies to Target Molecular and Cellular Heterogeneity in Pancreatic Cancer

<b><i>Background:</i></b> Pancreatic ductal adenocarcinoma (PDAC) remains a major challenge in cancer medicine and is characterized by a 5-year survival rate of &#x3c;10%. Compelling evidence suggests that the devastating disease outcome of PDAC patients is linked to a high degree of intra- and interindividual tumor heterogeneity, which is predominantly installed at the level of gene transcription. The cellular and molecular complexities of the disease explain the poor efficacy of “one-size-fits-all” therapeutic approaches in PDAC treatment and strongly argue for pursuing tailored therapeutic strategies to tackle PDAC. In a highly dynamic manner, a network of transcription factors and epigenetic regulatory proteins temporally and spatially control the diverse transcriptomic states determining PDAC heterogeneity. Given the reversibility of epigenetic processes, pharmacological intervention with key epigenetic drivers of PDAC heterogeneity appeals as a promising concept to shift the transcriptomic phenotype of PDAC toward a less aggressive and more chemosensible state. <b><i>Summary:</i></b> In this review, we discuss the chances and pitfalls of epigenetic treatment strategies in overcoming and shifting molecular and cellular PDAC heterogeneities in order to combat PDAC. To this end, we utilized the keywords “pancreatic cancer,” “heterogeneity,” and “epigenetics” to search for relevant articles on the database PubMed and selected interventional studies enrolling PDAC patients as displayed in clinicaltrails.gov to generate a synopsis of clinical trials involving epigenetic targeting. <b><i>Key Messages:</i></b> Targeting epigenetic regulators in PDAC represents a promising concept to reprogram molecular and cellular tumor heterogeneities in the pancreas and hence to modulate the PDAC phenotype in favor of a less aggressive and more therapy susceptible disease course. However, we just start to understand the complex interactions of epigenetic regulators in controlling PDAC plasticity, and a clinical breakthrough utilizing epigenetic targeting in PDAC patients has not been achieved yet. Nevertheless, increasing translational efforts which consider the pleiotropic effects of targeting epigenetic regulation in different cellular compartments of the tumor and that focus on the utility and sequence of combinatory treatment approaches might pave the way toward novel epigenetic treatment strategies in PDAC therapy.

Particularities of quality and safety control in cancer care (literature review)

Introduction. Last decade significant progress was made in the development of cancer care algorithms. In this regard, new challenges are constantly being presented to the quality control of medical activities in actual practice. Aims. To summarize the outlines regarding the most relevant criteria for assessing the quality in oncology. Material and methods. The PubMed database (Medline) was used to identify the relevant and reliable sources of literature. The thematic methodology for obtaining information was used. Results. In total, over 80 most significant publications were identified, thoroughly studied and analyzed. International experience indicates the advantages for assessing the quality of cancer care by determining and measuring certain indicators. The model for quality assessment proposed by Donabedian A. (1966) is broadly applied in current medical practice. This model distinguishes the following: 1) structural indicators, 2) process indicators, 3) outcome indicators. Feedback is critically important in the organization of the audit of medical activities. It allows one to adapt the assessment methods by focusing on the tasks immediately during the control process. Because of cancer heterogeneity, there are apart requirements for developing quality indicators for each specific type of cancer because of cancer heterogeneity. Conclusions. Monitoring of medical activities is a crucial pillar for a robust healthcare system. The introduction of essential, practical and specialized audit techniques helps to improve the quality and safety of medical technologies used in cancer care. There is an increasing need to develop optimal indicators and standard operating procedures for the control of cancer care.

From Cellular Infiltration Assessment to a Functional Gene Set-Based Prognostic Model for Breast Cancer

BackgroundCancer heterogeneity is a major challenge in clinical practice, and to some extent, the varying combinations of different cell types and their cross-talk with tumor cells that modulate the tumor microenvironment (TME) are thought to be responsible. Despite recent methodological advances in cancer, a reliable and robust model that could effectively investigate heterogeneity with direct prognostic/diagnostic clinical application remained elusive.ResultsTo investigate cancer heterogeneity, we took advantage of single-cell transcriptome data and constructed the first indication- and cell type-specific reference gene expression profile (RGEP) for breast cancer (BC) that can accurately predict the cellular infiltration. By utilizing the BC-specific RGEP combined with a proven deconvolution model (LinDeconSeq), we were able to determine the intrinsic gene expression of 15 cell types in BC tissues. Besides identifying significant differences in cellular proportions between molecular subtypes, we also evaluated the varying degree of immune cell infiltration (basal-like subtype: highest; Her2 subtype: lowest) across all available TCGA-BRCA cohorts. By converting the cellular proportions into functional gene sets, we further developed a 24 functional gene set-based prognostic model that can effectively discriminate the overall survival (P = 5.9 × 10−33, n = 1091, TCGA-BRCA cohort) and therapeutic response (chemotherapy and immunotherapy) (P = 6.5 × 10−3, n = 348, IMvigor210 cohort) in the tumor patients.ConclusionsHerein, we have developed a highly reliable BC-RGEP that adequately annotates different cell types and estimates the cellular infiltration. Of importance, the functional gene set-based prognostic model that we have introduced here showed a great ability to screen patients based on their therapeutic response. On a broader perspective, we provide a perspective to generate similar models in other cancer types to identify shared factors that drives cancer heterogeneity.

DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

Background Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. Results We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring. Conclusion DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453.

Exosomes in the Tumor Microenvironment: From Biology to Clinical Applications

Cancer is one of the most important health problems and the second leading cause of death worldwide. Despite the advances in oncology, cancer heterogeneity remains challenging to therapeutics. This is because the exosome-mediated crosstalk between cancer and non-cancer cells within the tumor microenvironment (TME) contributes to the acquisition of all hallmarks of cancer and leads to the formation of cancer stem cells (CSCs), which exhibit resistance to a range of anticancer drugs. Thus, this review aims to summarize the role of TME-derived exosomes in cancer biology and explore the clinical potential of mesenchymal stem-cell-derived exosomes as a cancer treatment, discussing future prospects of cell-free therapy for cancer treatment and challenges to be overcome.