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A New Biomarker for Skin Cancer: Unlocking the Secrets of Metastasis Risk and Treatment Opportunities

Cutaneous squamous cell carcinoma (cSCC), the most common type of metastatic skin cancer, affects a significant number of people worldwide. Despite its relatively low incidence rate compared to other types of cancers, cSCC is responsible for nearly 25% of annual skin cancer deaths. The prognosis for patients with metastatic cSCC is poor, with limited treatment options available.

Researchers have identified C5aR1 as a potential biomarker for metastasis risk and poor prognosis in patients with cSCC. This novel finding, published in The American Journal of Pathology, has significant implications for the diagnosis and treatment of this aggressive form of skin cancer.

The complement system, a part of the human innate immune system, plays a crucial role in tumor suppression by inducing inflammation or causing immunosuppression. However, studies have shown that the complement system can also contribute to tumor progression and metastasis. This complex interplay between the complement system and cancer cells has prompted researchers to investigate the interaction between C5a (a signaling molecule) and its protein receptor C5aR1.

The study’s findings reveal that C5a binds to C5aR1, activating signaling pathways within the cell, leading to changes in cell behavior. The investigators examined C5aR1 in the context of cSCC progression and metastasis by combining in vitro 3D spheroid co-culture of cSCC cells and skin fibroblasts, human cSCC xenograft tumors grown in SCID mice, and a large panel of patient-derived tumor samples.

The results showed that C5aR1 expression is linked to metastasis risk and poor survival in patients with cSCC. High C5aR1 expression was observed in both tumor cells and stromal fibroblasts, suggesting that the interplay between tumor cells and their surroundings plays a crucial role in cancer progression.

The researchers concluded that C5aR1 is a potential metastatic risk marker, a novel prognostic biomarker, and promising therapeutic target for cSCC. This discovery has significant implications for the diagnosis and treatment of this aggressive form of skin cancer, offering new hope for patients and their families.

In conclusion, the identification of C5aR1 as a potential biomarker for metastasis risk and poor prognosis in patients with cSCC is a significant breakthrough in the field of skin cancer research. Further studies are needed to fully understand the role of C5aR1 in cSCC progression and metastasis, but this discovery has the potential to unlock new treatment opportunities and improve patient outcomes.

SAVANA is a groundbreaking algorithm that uses machine learning to accurately identify structural variants and copy number aberrations in cancer genomes. This innovative tool has been developed to overcome the limitations of existing analysis tools, which often fall short when analyzing long-read sequencing data. The complex structure of cancer genomes means that standard analysis tools can lead to false-positive results and unreliable interpretations of the data.

Researchers at EMBL’s European Bioinformatics Institute (EMBL-EBI) and the R&D laboratory of Genomics England have developed SAVANA in collaboration with clinical partners at University College London (UCL), the Royal National Orthopaedic Hospital (RNOH), Instituto de Medicina Molecular João Lobo Antunes, and Boston Children’s Hospital. The algorithm was tested across 99 human tumour samples and has shown remarkable accuracy in distinguishing between true cancer-related genomic alterations and sequencing artefacts.

“SAVANA changes the game,” said Isidro Cortes-Ciriano, Group Leader at EMBL-EBI. “By training the algorithm directly on long-read sequencing data from cancer samples, we created a new method that can tell the difference between true cancer-related genomic alterations and sequencing artefacts, thereby enabling us to elucidate the mutational processes underlying cancer using long-read sequencing with unprecedented resolution.”

The team’s focus was clear: create a tool sophisticated enough to characterise complex cancer genomes but practical enough for clinical use. SAVANA can accurately distinguish somatic structural variants, copy number aberrations, tumour purity, and ploidy – all key to understanding tumour biology and guiding clinical treatment decisions.

Its rapid analysis and robust error correction make SAVANA well suited for clinical use. The method was recently applied to study osteosarcoma, a rare and aggressive bone cancer that mostly affects young people, where it helped researchers uncover new genomic rearrangements, providing novel insights into how osteosarcoma evolves and progresses.

“The capability to accurately detect structural variants is transformative for clinical diagnostics,” said Adrienne Flanagan, Professor at UCL, Consultant Histopathologist at RNOH. “SAVANA could help us confidently identify genomic alterations relevant for diagnosis and prognosis. Ultimately, this means we would be better placed to deliver personalised treatments for cancer patients.”

The UK is investing significantly in genomic sequencing technologies as part of the NHS Genomic Medicine Service. This initiative aims to improve diagnostic accuracy and support personalised cancer treatments. However, investments in clinical genomics will only achieve their intended impact if genomic data are interpreted accurately.

“Using SAVANA will ensure clinicians receive accurate and reliable genomic data, enabling them to confidently integrate advanced genomic sequencing methods such as long-read sequencing into routine patient care,” said Greg Elgar, Director of Sequencing R&D at Genomics England.

SAVANA is being deployed as part of nationwide initiatives, such as the UK Stratified Medicine Paediatrics project funded by Cancer Research UK and Children With Cancer UK, and co-led by Cortes-Ciriano. This project aims to develop more efficacious and less toxic treatments for childhood cancers using advanced sequencing technologies to better understand tumour biology and monitor disease recurrence.

Additionally, SAVANA is being used in Societal, Ancestry, Molecular and Biological Analyses of Inequalities (SAMBAI), a Cancer Grand Challenges funded project aimed at addressing cancer disparities in recent African heritage populations.

The Johns Hopkins Kimmel Cancer Center has made groundbreaking advancements in developing an artificial intelligence (AI) technique for detecting DNA fragments shed by tumors and circulating in a patient’s blood, potentially leading to better treatment outcomes for patients with pancreatic cancer. The innovative method, called ARTEMIS-DELFI, can identify therapeutic responses and was found to be more accurate than existing clinical and molecular markers.

The study, published in Science Advances, involved testing the ARTEMIS-DELFI approach in blood samples from two large clinical trials of pancreatic cancer treatments. Researchers discovered that this AI-driven technique could predict patient outcomes better than imaging or other current methods just two months after treatment initiation. The team found that the simpler and potentially more broadly applicable ARTEMIS-DELFI was a superior test compared to another method, called WGMAF.

Victor E. Velculescu, M.D., Ph.D., senior study author and co-director of the cancer genetics and epigenetics program at the cancer center, emphasized that time is crucial when treating patients with pancreatic cancer. Many patients receive diagnoses at a late stage, when cancer may progress rapidly. The team wants to know as quickly as possible whether a therapy is working or not, enabling them to switch to another treatment if it’s not effective.

Currently, clinicians use imaging tools like CT scans and MRIs to monitor cancer treatment response and tumor progression. However, these methods can produce results that are less accurate for patients receiving immunotherapies. In the study, Velculescu and his colleagues tested two alternate approaches to monitoring treatment response in patients participating in the phase 2 CheckPAC trial of immunotherapy for pancreatic cancer.

One approach analyzed DNA from tumor biopsies as well as cell-free DNA in blood samples to detect a treatment response, called WGMAF. The other used machine learning to scan millions of cell-free DNA fragments only in the patient’s blood samples, resulting in the ARTEMIS-DELFI method. Both approaches were able to detect which patients were benefiting from the therapies.

However, not all patients had tumor samples, and many patients’ tumor samples contained a small fraction of cancer cells compared to normal pancreatic and other cells, confounding the WGMAF test. The team validated that ARTEMIS-DELFI was an effective treatment response monitoring tool in a second clinical trial called the PACTO trial.

The study confirmed that ARTEMIS-DELFI can identify therapeutic responses in patients with pancreatic cancer just two months after treatment initiation, providing valuable insights for clinicians to make informed decisions. The team hopes that this innovative AI-driven technique will revolutionize the way we monitor and treat pancreatic cancer, ultimately improving patient outcomes.