bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2022‒07‒03
seven papers selected by
Sergio Marchini
Humanitas Research

  1. NPJ Precis Oncol. 2022 Jun 29. 6(1): 47
      Hormone receptor expression is a characteristic of low-grade serous ovarian carcinoma (LGSOC). Studies investigating estrogen receptor (ER) and progesterone receptor (PR) expression levels suggest its prognostic and predictive significance, although their associations with key molecular aberrations are not well understood. As such, we sought to describe the specific genomic profiles associated with different ER/PR expression patterns and survival outcomes in a cohort of patients with advanced disease. The study comprised fifty-five advanced-staged (III/IV) LGSOCs from the Canadian Ovarian Experimental Unified Resource (COEUR) for which targeted mutation sequencing, copy-number aberration, clinical and follow-up data were available. ER, PR, and p16 expression were assessed by immunohistochemistry. Tumors were divided into low and high ER/PR expression groups based on Allred scoring. Copy number analysis revealed that PR-low tumors (Allred score <2) had a higher fraction of the genome altered by copy number changes compared to PR-high tumors (p = 0.001), with cancer genes affected within specific loci linked to altered peptidyl-tyrosine kinase, MAP-kinase, and PI3-kinase signaling. Cox regression analysis showed that ER-high (p = 0.02), PR-high (p = 0.03), stage III disease (p = 0.02), low residual disease burden (p = 0.01) and normal p16 expression (p<0.001) were all significantly associated with improved overall survival. This study provides evidence that genomic aberrations are linked to ER/PR expression in primary LGSOC.
  2. Genome Med. 2022 Jun 27. 14(1): 68
      Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact and viable from tissue. This has precluded many cell types from study and largely destroys the spatial context that could otherwise inform analyses of cell identity and function. An increasing number of commercially available platforms now facilitate spatially resolved, high-dimensional assessment of gene transcription, known as 'spatial transcriptomics'. Here, we introduce different classes of method, which either record the locations of hybridized mRNA molecules in tissue, image the positions of cells themselves prior to assessment, or employ spatial arrays of mRNA probes of pre-determined location. We review sizes of tissue area that can be assessed, their spatial resolution, and the number and types of genes that can be profiled. We discuss if tissue preservation influences choice of platform, and provide guidance on whether specific platforms may be better suited to discovery screens or hypothesis testing. Finally, we introduce bioinformatic methods for analysing spatial transcriptomic data, including pre-processing, integration with existing scRNA-seq data, and inference of cell-cell interactions. Spatial -omics methods are already improving our understanding of human tissues in research, diagnostic, and therapeutic settings. To build upon these recent advancements, we provide entry-level guidance for those seeking to employ spatial transcriptomics in their own biomedical research.
  3. ACS Biomater Sci Eng. 2022 Jun 28.
      The leading cause of gynecological cancer-related morbidity and mortality is ovarian cancer (OC), which is dubbed a silent killer. Currently, OC is a target of intense biomarker research, because it is often not discovered until the disease is advanced. The goal of OC research is to develop effective tests using biomarkers that can detect the disease at the earliest stages, which would eventually decrease the mortality, thereby preventing recurrence. Therefore, there is a pressing need to revisit the existing biomarkers to recognize the potential biomarkers that can lead to efficient predictors for the OC diagnosis. This Perspective covers an update on the currently available biomarkers used in the triaging of OC to gain certain insights into the potential role of these biomarkers and their estimation that are crucial to the understanding of neoplasm progression, diagnostics, and therapy.
    Keywords:  CA125; HE4; RMI; ROMA; biomarker; electrochemical detection; ovarian cancer
  4. Adv Exp Med Biol. 2022 ;1379 171-203
      Organs-on-chips are microfluidic tissue-engineered models that offer unprecedented dynamic control over cellular microenvironments, emulating key functional features of organs or tissues. Sensing technologies are increasingly becoming an essential part of such advanced model systems for real-time detection of cellular behavior and systemic-like events. The fast-developing field of organs-on-chips is accelerating the development of biosensors toward easier integration, thus smaller and less invasive, leading to enhanced access and detection of (patho-) physiological biomarkers. The outstanding combination of organs-on-chips and biosensors holds the promise to contribute to more effective treatments, and, importantly, improve the ability to detect and monitor several diseases at an earlier stage, which is particularly relevant for complex diseases such as cancer. Biosensors coupled with organs-on-chips are currently being devised not only to determine therapy effectiveness but also to identify emerging cancer biomarkers and targets. The ever-expanding use of imaging modalities for optical biosensors oriented toward on-chip applications is leading to less intrusive and more reliable detection of events both at the cellular and microenvironment levels. This chapter comprises an overview of hybrid approaches combining organs-on-chips and biosensors, focused on modeling and investigating solid tumors, and, in particular, the tumor microenvironment. Optical imaging modalities, specifically fluorescence and bioluminescence, will be also described, addressing the current limitations and future directions toward an even more seamless integration of these advanced technologies.
    Keywords:  Bioluminescence; Biosensors; Cancer; Imaging; Microfluidic systems; Organs-on-chips; Tumor microenvironment