New research from Memorial Sloan Kettering Cancer Center (MSK) marks a potential advance against RAS-driven cancers; breaks down data silos to better predict cancer outcomes with the help of artificial intelligence (AI); identifies two enzymes vital for maintaining brain health; uncovers how changes to "helper" proteins drive cancer cell survival; develops a new model for investigating lung cancer metastasis; and uses AI to improve outcome predictions in sarcoma.
MSK researchers make significant step toward development of new anti-RAS therapies
Each year, more than 3 million people are diagnosed with cancers driven by mutations in three RAS-family genes: KRAS, NRAS, and HRAS. These mutations impair the GTPase activity of RAS (an enzymatic process critical for cellular signaling and functions), leading to runaway cell proliferation. For decades, scientists' efforts to target this process to restore the normal function of mutant RAS proteins have been unsuccessful.
Now an MSK research team has identified a therapeutic approach that has shown promise in preclinical models. In a new study, scientists from the lab of Piro Lito, MD, PhD -- co-led by Antonio Cuevas-Navarro, PhD, and Yasin Pourfarjam, PhD -- demonstrated that certain inhibitors can not only prevent RAS from binding to its downstream targets or signaling partners, but, unexpectedly, can also improve the impaired GTP hydrolysis caused by the mutations.
"This study lays the foundation for developing a new class of drugs to inhibit cancer growth by restoring the ability of mutant RAS proteins to break down the nucleotide GTP," Dr. Lito says. "This is perhaps the most physiologic way to inactivate this important cancer-causing protein, because it repairs the defect caused by the mutation." Read more in Nature.
MSK-CHORD breaks down data silos to better predict cancer outcomes
An MSK research team is demonstrating that cancer outcome predictions can be improved by breaking down hospitals' traditional data silos and analyzing the information -- including physicians' clinical notes -- with the help of artificial intelligence (AI).
A new study describes a real-time, automated approach developed at MSK that brings together doctors' free-text notes, clinical treatment and outcomes data, patient demographic data, and tumor genomic data from the MSK-IMPACT platform to identify biomarkers that can predict outcomes and likely responses to therapy. Dubbed MSK-CHORD (for Clinicogenomic Harmonized Oncologic Real-World Dataset), the effort is the largest of its kind, combing data from nearly 25,000 patients with non-small cell lung, breast, colorectal, prostate, and pancreatic cancers. The study was led by co-first authors Justin Jee, MD, PhD, Christopher Fong, PhD, Karl Pichotta, PhD, Thinh Ngoc Tran, PhD, and Anisha Luthra, and overseen by senior author Nikolaus Schultz, PhD, Director of MSK's Cancer Data Science Initiative.
The team found that cancer outcome predictions based on MSK-CHORD data outperformed those based on genomic data or cancer stage alone. And by analyzing more than 700,000 radiology reports, MSK-CHORD was able to uncover predictors of metastasis to specific organ sites. Additionally, MSK-CHORD's size and rich annotations led the team to identify mutations in the SETD2 gene as an uncommon but promising biomarker of immunotherapy response in lung adenocarcinoma -- a finding that was corroborated in multiple independent datasets.
"Our results highlight the power of natural language processing and the impact of bringing together a multitude of data streams to better predict patient outcomes," says Dr. Jee, a thoracic medical oncologist at MSK. "It is our hope that MSK-CHORD will fuel further research into the relationships between genomic data and real-world outcomes in cancer," Dr. Schultz adds. Dozens of clinicians and researchers from across MSK came together to share expertise, and to develop the natural language processing models, AI risk models, and engineering infrastructure required to support the effort, they note. Read more in Nature.
MSK researchers identify two enzymes vital for maintaining brain health
Lipids are important components of cells, especially in the brain. In a recent study, researchers at MSK's Sloan Kettering Institute focus on bis(monoacylglycero)phosphate (BMP), a lipid uniquely localized to lysosomes, which are organelles essential for recycling cellular material. BMP is crucial in maintaining lysosomal function, and disturbances in its levels lead to neurodegenerative diseases. Yet how this important cellular component is made was a mystery for some 50 years.
Researchers from the Farese and Walther Lab in MSK's Cell Biology Program have now identified that the enzymes PLD3 and PLD4 are central to BMP synthesis. These enzymes ensure the integrity of BMP in the lysosome, protecting it from degradation. Removing the enzymes caused BMP levels to drop precipitously in cell and mouse models, leading to the accumulation of gangliosides and causing other abnormalities in the lysosome. The team, led by postdoctoral fellow Shubham Singh, PhD, found that mutations in the PLD3 enzyme, which have been linked to neurodegenerative diseases such as Alzheimer's, reduced the ability to produce BMP. Overall, the findings provide a more complete picture of BMP's biosynthetic pathway and shed new light on some of the underpinnings of neurodegenerative disease. Read more in Cell; the work was also highlighted by the Howard Hughes Medical Institute.
How changes to 'helper' proteins drive cancer cell survival
Inside our cells, special helper proteins called chaperones ensure that other proteins fold correctly and function as they should. But in cancer cells, these chaperones can group together and form faulty networks that support the growth and survival of the tumor. In 2016, MSK scientist Gabriela Chiosis, PhD, discovered these harmful networks of chaperones and named them epichaperomes.
In a new study, Dr. Chiosis and her team have uncovered a key process that drives the formation of these faulty networks: chemical modifications to a chaperone protein called HSP90. Specifically, they identified two changes, or phosphorylations, at certain points on HSP90 that are crucial for triggering epichaperome formation. These changes make it easier for HSP90 to interact with other chaperones, leading to the creation of a supportive environment that allows cancer cells to thrive under stress.
Drugs that target these faulty networks are already being developed for cancer and other diseases, such as Alzheimer's. These latest findings could help guide the development of even more effective treatments for diseases linked to these malfunctioning protein networks. Read more in Nature Communications.
A new model for investigating lung cancer metastasis
Over the past decade, researchers have made important advances in cancer science using patient-derived organoids (PDOs), which are 3D models of tumor cells grown in a lab from patient tissue samples. Using PDOs from lung adenocarcinoma tissue, a team led by MSK thoracic surgeon David Jones, MD, has established the first in vivo model to investigate human lung cancer metastasis.
This is particularly important because nearly 50% of people with surgically resected early-stage lung cancer develop metastasis in distant organs, a key reason lung cancer remains the leading cause of cancer death worldwide.
This effort used specific pathologic and genomic features of the primary tumors to improve upon previous efforts to generate lung adenocarcinoma PDOs, which have had a low success rate. The research determined the new PDO metastasis model is useful as a tool for the study of tumor evolution, to assess personalized drug efficacy and reveal mechanisms of resistance to targeted therapies, and to help personalize strategies for the use of immunotherapy.
Dr. Jones, Chief of the Thoracic Service, Department of Surgery, and Co-Director of the Fiona and Stanley Druckenmiller Center for Lung Cancer Research, says: "Creating these PDO models directly from our patients' lung cancer cells adds yet another important tool for us to better understand the biology of lung cancer and to identify ways to better predict its behavior and treat the cancer with specific therapies." Read More in Cell Reports Medicine.
MSK-developed AI model may better predict outcomes in sarcoma
A first-of-its kind artificial intelligence (AI) model developed at MSK outperformed other approaches in predicting overall survival and the risk of distant metastases in people with sarcoma, according to a new study. The research team -- which was led by former orthopedic oncology fellow Anthony Bozzo, MD (now at McGill University), and overseen by surgeon John Healey, MD -- developed a multimodal neural network using data from nearly 300 patients, including 3D MRI images and clinical variables.
The model was better able to predict outcomes than using clinical information or imaging scans alone, and better than radiomics models, the team found. "This work represents MSK's first-ever published multimodal neural network and the first paper of its kind in sarcoma," Dr. Bozzo notes. "This work would not have been possible without data scientist Subrata Chatterjee, PhD, machine learning engineer Alex Hollingsworth, and the support of Avijit Chatterjee, PhD, who leads AI, machine learning, and NextGen Analytics at MSK." Efforts are underway to externally validate the model using data from other institutions, including McGill -- aiming to provide more accurate and personalized predictions that can effectively guide patient management and improve survival rates. Read more in npj Precision Oncology.