“Traditional” biomedical professionals, such as molecular biologists, physiologists, chemists,

pathologists, radiologists, and clinicians, may have complicated feelings about AI. AI is exceptionally

powerful, and its applications are pervasive in various fields, including data analysis, pathological

diagnosis, protein structure prediction, drug design, behavior prediction, surgical design, and

bioengineering projects. If you are a devoted enthusiast of biomedical sciences, AI’s potential is sure to

excite you. However, as you read research papers, attend conference presentations, or search for jobs

on LinkedIn, the term “AI” may grab your attention more than you like. You might feel somewhat

outdated and anxious about your role as a “traditional” researcher. But no worries. For the time being,

AI remains a tool that relies on other sources of data for quantification, data that are gathered from a

variety of “traditional” research models, including histological images, radiological scans, flow

cytometry, genomics, proteomics, and spatial transcriptomics analysis, among others.

Each researcher is unique, and it is important to remember that collaborative efforts are often more

fruitful than disorderly competition. Here is a prime example: a recent extensive collaboration involved

researcher from various fields, including neurobiology, experimental therapeutics, anatomy and cell

biology, physiology, and bioinformatics, from research organizations in Spain and the US. Together, they

made significant contributions in developing innovative strategies for diagnosing brain metastasis on live

experimental mice. The leading-edge discoveries were published on September 11, 2023 in the

renowned journal Cancer Cell, under the title “Machine learning identifies experimental brain

metastasis subtypes based on their influence on neural circuits” (https://www.cell.com/cancer-

cell/pdf/S1535-6108(23)00250-7.pdf).

As we are aware, brain metastasis often afflicts cancer patients in the advanced stages of the disease,

with breast, melanoma, and lung cancer being the most common culprits. Unfortunately, the prognosis

of most patients who develop brain metastasis is not optimistic, with an average survival time of only

six months. However, early detection of brain metastasis and effective management of primary cancers

can significantly extent the patients’ life, in some cases, can lead to even a complete cure. Given that

brain metastasis can cause severe symptoms, including headaches, seizures, loss of balance, vision

disturbance, and cognitive disabilities, early and accurate diagnosis is paramount. Obviously,

conventional radiological scans are not sensitive enough required for this task.

So, what is the narrative behind this significant collaboration? The first phase was led by

electrophysiologists. Researchers inoculated breast, melanoma, and lung cancer cells to the brains of

the experimental mice to mimic brain metastasis. Subsequently, they collected local field potential (LFP)

recordings from regions adjacent to the tumor tissues, using a 16-channel linear probe implanted within

the mouse brains. The researchers observed a general reduction LFP from both cortical and

hippocampal areas, when brain metastasis from breast, melanoma, and lung cancer were present, with

the most pronounced LFP decrease from lung cancer metastasis. What is more intriguing is that the LFP

decrease was unrelated to tumor sizes and microenvironmental changes, directly challenging the

conventional belief that mass effects are the primary drivers of brain metastatic symptoms.

Consequently, the narrative progressed to its second phase, the molecular mechanisms. Employing

histological, immunohistology and electronic microscope analyses, researchers observed that the

neurons surrounding the metastatic tumors largely remained unaffected, and the neuropile structure

was well-preserved. However, there was a significant reduction of inhibitory synapses and calcium

activity in the peritumoral area of lung cancer metastasis when compared to the other two cancer types.

To unravel the mechanisms behind, researchers performed whole exome sequencing on all three cancer

cell lines and compared the differential gene expressions. They identified 51 genes with differential

expression in lung cancer cell line, particularly involved with neuronal communication. Among the 51

genes, transcription factor EGR1, known for its role in angiogenesis and modulation of synaptic

communication, stood out to be the top candidate for further investigation. Therefore, researchers

conducted single-cell RNA-seq on Egr1+ cancer cells on lung cancer metastatic tissues. This analysis

revealed a molecular signature involving the tumor necrosis factor alpha (TNFα) signaling via nuclear

factor kB in three out of the four Egr1+ cell clusters. Those findings provide further evidence that mass

effects do not alone account for all brain metastatic symptoms, instead, molecular signaling within the

tumor tissues may play a more critical role.

Now, it is time for the AI scientists to narrate the third and the final part of the story. Researchers

started by creating a vast dataset encompassing 492 samples, which incorporated electrophysiological

profiles with multiple category variables, including metastatic type, detection location (Left/Right

hemisphere), behavioral state (Run/Still), the origin of the data (Cortex/Hippo), and various circuit

oscillation wavelengths. And then, the researchers developed a generalized linear model to fit and

distinguish all the electrophysiological profiles. Moreover, through implementation of principal

component (PC) analysis, researchers identified 9 major directions which could explain a staggering 99%

of the data’s variance, which even include the capacity to discriminate brain metastasis subtypes.

The most exciting part followed: to train the model through repetitive iterations. Researchers initially

utilized five class predictors for training but eventually settled on Decision Tree Classifier, given its

superior accuracy. Astonishingly, Decision Tree Classifier could sensitively detect brain metastatic cells

as early as 7 days post implantation. What’ more, it could precisely identify metastatic subtypes

resulting from new types of lung cancer cells not included in the initial training dataset. Based on those

remarkable findings, researchers concluded that machine learning, based on electrophysiological

patterns, holds the potential to identify metastasis subtypes within the brain of live experimental mice.

And with that, we conclude the narrative, jointly presented by electrophysiologists, molecular biologists,

and AI scientists. A wonderful story, isn’t it? Personally, I believe that it could be even more engrossing if

the machine learning part had more interconnection with the “traditional” component, specifically, the

molecular signature segment. Nevertheless, the battle against cancer will be a long journey, while this

study remarkable, cannot encompass everything about the brain metastasis puzzle. What truly shines in

this study was the great collaboration among researchers from diverse fields; irrespective of whether

their backgrounds are more traditional or contemporary, each group exhibited their utmost expertise.

Moreover, let’s spend a moment to show our respect for the experimental animals that played

indispensable roles in this study. If you have diligently perused the materials and methods section of this

paper, you will undoubtedly appreciate the sacrifices these little creatures made on behalf of us,

humans. Even though each animal may not have had a name, as a collective, they deserve to be

remembered and acknowledged.