A New Deep Learning Approach Developed at MIT Identifies Undiagnosable Cancers by Taking a Closer Look the Gene Expression Programs Related to Early Cell Development and Differentiation

Cancer is partially a developmental illness, with malignancies named for the cell or tissue from which they originate. However, there is no systematic atlas of tumor sources. Identifying a patient’s precise type of cancer and its main site is the first step in deciding on the best course of treatment.

Despite extensive testing, the source of cancer cannot be determined in many situations. Oncologists must employ non-targeted medicines with severe side effects and poor survival rates.

Researchers at Massachusetts General Hospital (MGH) and the Koch Institute for Integrative Cancer Research at MIT may help classify cancers of unknown primary. Their work introduces a new deep learning method by closely examining the gene expression patterns related to early cell development and differentiation.

In part because of major changes to how their genes are expressed, cancer cells differ greatly from normal cells in terms of how they appear and behave. Machine learning is well suited to solving the problem of separating the changes in gene expression between various types of cancers from an unknown source. There is a tonne of data that hints at how and where certain tumors began, thanks to developments in single-cell profiling and initiatives to catalog various cell expression patterns in cell atlases.

The team concentrated the model on indicators of disrupted developmental pathways in cancer cells to achieve a compromise between lowering the number of features and capturing the most pertinent information. Cancer cells lose many of the specialized characteristics of a mature cell as the tumor grows. As they develop the capacity to multiply, change, and spread to new tissues, they also start to resemble embryonic cells in some respects. Numerous embryogenesis-related gene expression processes are known to be reactivated or dysregulated in cancer cells.

The Cancer Genome Atlas (TCGA), which contains gene expression data for 33 different tumor types, and the Mouse Organogenesis Cell Atlas (MOCA), which profiles 56 different trajectories of embryonic cells as they develop and differentiate, were compared by the researchers to find correlations between tumor and embryonic cells.

A machine learning model was created using the resulting map of correlations between developmental gene expression patterns in tumors and embryonic cells. The researchers assigned a numerical value to each component they created by dissecting the different components of the gene expression of tumor samples from the TCGA into discrete parts representing a distinct developmental trajectory stage. The Developmental Multilayer Perceptron (D-MLP) machine-learning model rates a tumor according to its developmental components and then forecasts its origin.

Following training, the D-MLP was used to analyze 52 fresh samples of difficult malignancies with unclear primary origins that could not be identified using the methods at hand. Over four years, starting in 2017, these cases were the most difficult ones encountered at MGH. It’s exciting to note that the model divided the tumors into four categories and produced forecasts and other data that may help doctors diagnose and treat these patients.

Additionally, the study’s thorough comparisons of tumor and embryonic cells provided encouraging and occasionally unexpected insights into the gene expression profiles of particular tumor types. Their findings imply that differences in developmental programs may one day be leveraged, like how genetic variants are frequently employed to generate tailored or customized cancer therapies.

The researchers plan to improve the model’s predictive ability by including additional types of data, particularly details from radiology, microscopy, and other tumor imaging techniques.

This Article is written as a research summary article by Marktechpost Staff based on the research paper 'Developmental Deconvolution for Classification of Cancer Origin'. All Credit For This Research Goes To Researchers on This Project. Check out the paper and reference article.

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