Modern medical research is beginning to challenge traditional methods of classifying cancers. The current approach, which categorizes cancers based on the areas of the body they affect, may not accurately represent the molecular and genetic mechanisms that drive these diseases. This realization has profound implications for the development of cancer treatments, particularly for metastatic cancers, which have the potential to respond to drugs designed to target diverse cancer types. Consequently, oncologists are advocating for a shift in how cancers are named, moving away from body parts and focusing more on the underlying molecular and genetic factors. This shift requires a change in thinking from patients, clinicians, and regulators alike.
The Need to Rethink Cancer Classification
As highlighted in a recent podcast by senior comment editor Lucy Odling-Smee and Fabrice AndrÃ© from the Institute Gustave Roussy, there is a growing need to rethink how we classify cancers. This discussion, featured on Nature, emphasizes that drugs developed for metastatic cancers have demonstrated efficacy across different cancer types. This suggests the need for a new naming convention, one that is reflective of the molecular and genetic mechanisms of cancers rather than their location in the body.
Delving into Molecular and Genetic Mechanisms of Cancer
Contemporary research is beginning to unravel the complex molecular and genetic mechanisms that underlie cancer. Studies exploring the use of methylated cell-free DNA as a biomarker for pancreatic cancer, the role of circular RNAs in cancers, and the molecular mechanism underlying chemoresistance in colorectal cancer, among others, are providing valuable insights. This research, available on Molecular Cancer, is leading the way in our understanding of cancer at a fundamental level, which in turn, is shaping our approach to cancer treatment.
Deep Learning in Genomics and Histopathology for Precision Oncology
The field of precision oncology is also benefiting from these advances. Systematic analysis of deep learning in genomics and histopathology is revealing how artificial intelligence, particularly deep learning, can be used to address challenges in cancer diagnosis and treatment. The use of deep learning in pathology and genomics allows for more precise identification of cancer biomarkers and the prediction of drug responses, further enhancing the potential for targeted cancer treatments.
Understanding Histological Subtypes of Bladder Cancer
Our growing understanding of the molecular basis of cancer is already transforming our view of certain cancer types. For instance, bladder cancer, a histologically and clinically heterogeneous disease, has several distinct subtypes such as micropapillary, plasmacytoid, small cell carcinoma, and sarcomatoid. Each of these subtypes exhibit distinct genomic alterations and biological properties primarily determined by specific gene expression profiles, as elaborated in Nature. This breakthrough knowledge is leading to the generation of new hypotheses for therapy and chemoresistance and the discovery of new therapeutic targets.
The Evolution of Cancer Genomics and Transcriptomics
As outlined in a review on ScienceDirect, the field of cancer genomics and transcriptomics has evolved significantly, moving from targeted profiling to swift sequencing of individual tumor genome and transcriptome. This evolution is helping to capture signatures representing biological features of tumors, enabling more precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. Such advances are heralding a new era of clinical applications that can improve diagnosis, prognosis, and treatment decisions in cancer patients.
In conclusion, the traditional method of naming cancers based on body parts may soon be a thing of the past. The shift towards classification based on molecular and genetic mechanisms is not just a semantic change. It represents a fundamental shift in our understanding of cancer and has the potential to revolutionize cancer treatment. As we continue to explore the intricacies of this dreadful disease, we must be prepared to embrace change, not just in our scientific approaches, but in our language and mindset as well.