CERVICAL CANCER

Patient-Derived Organoids Model Cervical Tissue Dynamics and Viral Oncogenesis in Cervical Cancer

Researchers developed the first patient-derived organoid model for cervical cancer, according to a new paper published in Cell Stem Cell (2021; https://doi.org/10.1016/j.stem.2021.03.012). They also modelled a healthy human cervix using organoids. The researchers used the organoid-based platform to study sexually transmitted infections for a herpes virus. The model can potentially also be used to study human papillomavirus (HPV). The researchers obtained human cervical tissue from either healthy patients or patients with different types of cervical cancer. From these tissues they grew organoids, which are tiny 3D structures of about half a millimeter in size that closely mimic organ function. The organoids derived from healthy tissue closely resemble the tissue architecture and gene expression profiles of the actual human cervix. Prospectively, the model could also be applied to study HPV and how that virus causes cancer. The organoids grown from cancerous tissue, called tumoroids, closely resemble actual tumors. They show mutation and gene expression profiles that are typical for cancer and they carry similar morphological abnormalities. The researchers also found that the tumoroids respond differently to common chemotherapeutics, paving the way to the era of precision medicine. The organoids can be derived via patients' Pap-brush material. The study shows that the tiny bit of tissue obtained from this procedure is sufficient for starting an organoid culture. This opens up the possibility to not only look at fixed cells under the microscope, but also do in-depth analyses of the living cells that might be on their way to become cancerous. Additionally, given that the Pap-brush is non-invasive for patients, the new organoid model provides the research community with a relatively easy access to the tissue of interest and a new model system.

IMMUNE CHECKPOINT INHIBITORS

Epidemiology and Risk Factors for the Development of Cutaneous Toxicities in Patients Treated With Immune Checkpoint Inhibitors: A United States Population-Level Analysis

Immune checkpoint inhibitors, which boost the immune system's response against tumor cells, have transformed treatment for many advanced cancers, but short-term clinical trials and small observational studies have linked the medications with various side effects, most commonly involving the skin. A more comprehensive, population-level analysis now provides a thorough look at the extent of these side effects and provides insights on which patients may be more likely to experience them (J Am Acad Dermatol 2021; https://doi.org/10.1016/j.jaad.2021.03.094). The study involved analyzing information from a national health insurance claims database pertaining to 8,637 patients who were treated with immune checkpoint inhibitors, as well as an equal number of patients with cancer who did not receive these medications. The overall incidence of skin-related side effects was 25.1 percent, with a median time of onset of 113 days. Researchers "found that only 10 of more than 40 skin conditions previously reported to be linked to immune checkpoint inhibitors are actually seen at a higher incidence among patients on these medications compared with other patients who were matched by demographics, cancer type, and comorbidities." These conditions manifested with symptoms of itching, inflammation, rash, skin color loss, swelling, or blisters. Patients with melanoma or kidney cancer and those receiving multiple types of immune checkpoint inhibitors were at an especially high risk of developing these skin problems. The investigators' real-world data also found that skin-related symptoms tended to arise later than those noted in clinical trials. In addition, they found that clinicians often prescribed systemic corticosteroids to treat them even though these drugs should generally be avoided due to concerns that they may blunt the anti-tumor effects of immunotherapy.

MACHINE LEARNING

Integration of Multiomics Data With Graph Convolutional Networks to Identify New Cancer Genes and Their Associated Molecular Mechanisms

A new algorithm can predict which genes cause cancer, even if their DNA sequence is not changed, according to a team of researchers who combined a wide variety of data, analyzed it with artificial intelligence, and identified numerous cancer genes (Nat Mach Intell 2021; https://doi.org/10.1038/s42256-021-00325-y). The researchers developed a new algorithm using machine learning technology to identify 165 previously unknown cancer genes. The sequences of these genes are not necessarily altered-apparently a dysregulation of these genes can lead to cancer. All of the newly identified genes interact closely with well-known cancer genes and have been shown to be essential for the survival of tumor cells in cell culture experiments. The algorithm, dubbed EMOGI for Explainable Multi-Omics Graph Integration, can also explain the relationships in the cell's machinery that make a gene a cancer gene. The researchers explained that the software integrates tens of thousands of datasets generated from patient samples. These contain information about DNA methylations, the activity of individual genes, and the interactions of proteins within cellular pathways, in addition to sequence data with mutations. In these data, a deep-learning algorithm detects the patterns and molecular principles that lead to the development of cancer. Researchers merged sequence data that reflect faults in the genetic sequence with information that represents events inside the cell. Initially, the scientists confirmed that mutations, or the multiplication of segments of the genome, are indeed the main drivers of cancer. Then, in a second step, they pinpointed gene candidates that are in a less direct context to the actual cancer-driving gene. The computer analyzed tens of thousands of different network maps from 16 different cancer types, each containing between 12,000 and 19,000 data points.

MEDICAL FINANCIAL HARDSHIP

Medical Financial Hardship in Survivors of Adolescent and Young Adult Cancer in the United States

A new study finds higher medical financial hardship in adult survivors of adolescent and young adult (AYA) cancers than in adults without a history of cancer in the U.S. (JNCI 2021; https://doi.org/10.1093/jnci/djab013). Experts have known that cancer and its treatment can cause significant financial hardship to cancer survivors and their families. However, the long-term economic implications for adult survivors of AYA cancers were not fully understood. In this study, investigators used data from the National Health Interview Survey (2010-2018) and analyzed responses from adult (>18 years) survivors of AYA cancers (ages 15-39 at diagnosis) and adults without a cancer history. The study explored the various aspects of financial hardships, including material (e.g., ability to pay bills), psychological (e.g., worries about medical bills), and behavioral (e.g., delaying or foregoing medical care) measures. Adult survivors of AYA cancers were more likely than adults without a cancer history to report material and behavioral financial hardship, including problems paying medical bills or delaying or forgoing care because of cost. Adult survivors of AYA cancers were more likely to report greater intensity of medical financial hardship than their counterparts without a cancer history. Adult survivors of AYA cancers were more likely to report cost-related medication non-adherence, such as skipping medication doses, taking less medication, and delaying filling a prescription to save money. As the incidences of AYA cancers increase, understanding the spectrum of medical financial hardship is critical to those caring for and designing policies for adult survivors of AYA cancers, and in guiding ongoing research in this area.