In general, sequencing a complete transcript nowadays is easy using next generation sequencing (NGS) techniques. Just retro transcribe, make the cDNA library, add the adaptors (depending on which sequencing technique are you using) and sequence. On the other hand, if you want to know full protein expression, that’s kind of hard. The most used thechnique to determine different protein levels are based on the use of antibodies. So for that you should have a support whith thousands of different antibodies detecting each one a different protein to determine translational levels from RNA. This is way more expensive than transcriptics. A lot of people tend to suppose that more RNA means more protein. That’s one approach, but studies have revealed that translational control may be even more important than transcriptional control over the final amount of protein you got. So yes, making a proteomics full study would be a much better idea, but way more expensive as well. Ideally, you should also study Interactome, but as I know, there is no a “massive interactome” protocol.

There are a few reasons for this. First, it is easier to measure changes in gene expression than it is to measure changes in protein levels. Second, transcriptomic changes are more likely to be associated with changes in tumor behavior than changes in protein levels. Finally, transcriptomic data can be used to generate hypotheses about the mechanisms underlying tumor behavior, which can then be tested using other experimental techniques.

Well in clinical practice, for example in hematological malignancies, they usually do different type of sequencing called targeted Sequencing for specific genes which they know would be of interest for that class of blood disease. Same with other types of solid cancers looking for biomarkers to classify the patients, in order to know which treatment protocol would be most suitable.

In clinical trial however, this is still learning process. So they would want to collect as much data as possible from the patients to categorize them into which patient group had the most robust effect when using the drug and what kind of side effect you would expect with these category of people. That's why the whole transcriptomic analysis and possibly other highthrouput data as well. Saying so, before admission to clinical trials and based on previous pre-clinical data, some companies might place requirement for certain biomarker in the group of patient to be admitted to the clinical trial.

Solid tumor oncology clinical trials commonly use transcriptomics instead of measuring specific protein biomarkers for a few reasons. First, transcriptomics provides a more comprehensive view of the changes happening at the gene level in response to a treatment. This can give researchers a better understanding of how a treatment is affecting the tumor and whether it is working. Additionally, transcriptomics is less expensive and more accessible than protein biomarker testing.

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I like this response. Your first point resonates with me, but on the second point: given the cost of clinical trials and the small number of patients in oncology trials in particular, is the cost difference really so extreme?

Transcriptome is undoubtedly fascinating in oncology, but the real question is how that translates into actual proteins, right? Like measuring the amount of time each student studies each textbook page for a test rather than capturing the actual results of the test; you get more data one way, but the data you really want is the smaller set, right?

Interesting point about mechanisms of tumor behavior.

Yeah okay, didn't understand your question. The problem with proteomics is you need alot of starting material in order to get data, specially when talking about discovery highthrouput data which sometimes is not available in case of patients in clinial trials. In cases of clinical practice you do not need that much, as different techniques are being used such as flow cytometry, which works for few biomarkers but not for highthrouput discovery data. However that limitation is being approached now from several directions including spatial proteomics.

On the other hand, transcriptomic analysis can be done with very small amount of starting material, and with much cheaper price. Simply as this amount will be amplified during the procedure, which cannot be achieved for proteomics.

Did that answer your question??

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