Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

https://acsjournals.onlinelibrary.wiley.com/doi/epdf/10.3322/caac.21552

Artificial intelligence in cancer imaging: Clinical challenges and applications.

Circular RNAs (circRNAs) are a large class of transcripts in the mammalian genome. Although the translation of circRNAs was reported, additional coding circRNAs and the functions of their translated products remain elusive. Here, we demonstrate that an endogenous circRNA generated from a long noncoding RNA encodes regulatory peptides. Through ribosome nascent-chain complex-bound RNA sequencing (RNC-seq), we discover several peptides potentially encoded by circRNAs. We identify an 87-amino-acid peptide encoded by the circular form of the long intergenic non-protein-coding RNA p53-induced transcript (LINC-PINT) that suppresses glioblastoma cell proliferation in vitro and in vivo. This peptide directly interacts with polymerase associated factor complex (PAF1c) and inhibits the transcriptional elongation of multiple oncogenes. The expression of this peptide and its corresponding circRNA are decreased in glioblastoma compared with the levels in normal tissues. Our results establish the existence of peptides encoded by circRNAs and demonstrate their potential functions in glioblastoma tumorigenesis.

/pdf/a-peptide-encoded-by-circular-form-of-linc-pint-suppresses-3x5gicqpj5.pdf

A peptide encoded by circular form of LINC-PINT suppresses oncogenic transcriptional elongation in glioblastoma

Interrogation of the Microenvironmental Landscape in Brain Tumors Reveals Disease-Specific Alterations of Immune Cells

Glioblastoma ranks among the most lethal of all human cancers. Glioblastomas display striking cellular heterogeneity, with stem-like glioblastoma stem cells (GSCs) at the apex. Although the original identification of GSCs dates back more than a decade, the purification and characterization of GSCs remains challenging. Despite these challenges, the evidence that GSCs play important roles in tumor growth and response to therapy has grown. Like normal stem cells, GSCs are functionally defined and distinguished from their differentiated tumor progeny at core transcriptional, epigenetic, and metabolic regulatory levels, suggesting that no single therapeutic modality will be universally effective against a heterogenous GSC population. Glioblastomas induce a systemic immunosuppression with mixed responses to oncoimmunologic modalities, suggesting the potential for augmentation of response with a deeper consideration of GSCs. Unfortunately, the GSC literature has been complicated by frequent use of inferior cell lines and a lack of proper functional analyses. Collectively, glioblastoma offers a reliable cancer to study cancer stem cells to better model the human disease and inform improved biologic understanding and design of novel therapeutics.

/pdf/glioblastoma-stem-cells-lessons-from-the-tumor-hierarchy-in-rh58ov36rq.pdf

Glioblastoma stem cells: lessons from the tumor hierarchy in a lethal cancer

The complexity of glioblastoma multiforme, the most common and lethal variant of gliomas, is reflected by cellular and molecular heterogeneity at both the inter- and intra-tumoural levels. Molecular subtyping has arisen in the past two decades as a promising strategy to give better predictions of glioblastoma multiforme evolution, common disease pathways, and rational treatment options. The Cancer Genome Atlas network initially identified four molecular subtypes of glioblastoma multiforme: proneural, neural, mesenchymal and classical. However, further studies, also investigated glioma stem cells, have only identified two to three subtypes: proneural, mesenchymal and classical. The proneural-mesenchymal transition upon tumour recurrence has been suggested as a mechanism of tumour resistance to radiation and chemotherapy treatment. Glioblastoma multiforme patients with the mesenchymal subtype tend to survive shorter than other subtypes when analysis is restricted to samples with low transcriptional heterogeneity. Although the mesenchymal signature in malignant glioma may seem at odds with the common idea of the ectodermal origin of neural-glial lineages, the presence of the mesenchymal signature in glioma is supported by several studies suggesting that it can result from: (i) intrinsic expression of tumour cells affected with accumulated genetic mutations and cell of origin; (ii) tumour micro-environments with recruited macrophages or microglia, mesenchymal stem cells or pericytes, and other progenitors; (iii) resistance to tumour treatment, including radiotherapy, antiangiogenic therapy and possibly chemotherapy. Genetic abnormalities, mainly NF1 mutations, together with NF-κB transcriptional programs, are the main driver of acquiring mesenchymal-signature. This signature is far from being simply tissue artefacts, as it has been identified in single cell glioma, circulating tumour cells, and glioma stem cells that are released from the tumour micro-environment. All these together suggest that the mesenchymal signature in glioblastoma multiforme is induced and sustained via cell intrinsic mechanisms and tumour micro-environment factors. Although patients with the mesenchymal subtype tend to have poorer prognosis, they may have favourable response to immunotherapy and intensive radio- and chemotherapy.

/pdf/the-landscape-of-the-mesenchymal-signature-in-brain-tumours-31pth0w6n6.pdf

The landscape of the mesenchymal signature in brain tumours

Glioblastoma is an aggressive brain tumor that carries a poor prognosis. The tumor’s molecular and cellular landscapes are complex, and their relationships to histologic features routinely used for diagnosis are unclear. We present the Ivy Glioblastoma Atlas, an anatomically based transcriptional atlas of human glioblastoma that aligns individual histologic features with genomic alterations and gene expression patterns, thus assigning molecular information to the most important morphologic hallmarks of the tumor. The atlas and its clinical and genomic database are freely accessible online data resources that will serve as a valuable platform for future investigations of glioblastoma pathogenesis, diagnosis, and treatment.

/pdf/an-anatomic-transcriptional-atlas-of-human-glioblastoma-4dc8tp99u3.pdf

Zack Riley

Papers

An anatomic transcriptional atlas of human glioblastoma