Predictive markers of BRAF targeted therapy effectiveness!

 

Title first because - by page 2 I was thinking - 

1)  Can I get another espresso without waking my kid or his puppy up? 

and 

2) What a weird and crappy experimental design! I do need that espresso because I'm clearly misreading Table 1. 

24 solid tumors were analyzed? Across both sexes and a wide age range? They are all tumors? Like no healthy matched controls? Wait - are those tumors from different organs? Weird, right? 

The reason I'm continuing to type isn't (solely) because I'm procrastinating on 10 things I need to be doing. I'm continuing to type because it fucking worked! 

The question was totally a spotlight into the dark to illuminate an important area. The question is something like: We give people these BRAF targeted therapeutics when we find a BRAF 600E mutation and we don't know anything else about whether that's was the best idea or how well it would work. So they had this small set of frozen tumors from people who had this BRAF targeted therapy and they did RNASeq and proteomics (TIMSTOF) on them. They also know what regimen of drugs the patients were on and how well the regimen worked as broken down in how long the therapy kept the cancer from recurring.

The RNASeq pointed out a bunch of mutations but - no surprise to anyone here - it doesn't appear to have pointed out a protein that appears to coincide, maybe even correlate (unclear), with the time that the patient was in remission.

I guess the lesson here is something like - look - sometimes samples are super precious and you just don't have the numbers to assemble a cohort that makes sense - that doesn't mean you shouldn't take a look?

ABRF 2025 is next week! Don't miss the proteomics sessions!

Hey! Are you going to ABRF2025? It starts in 4 days! I should pick a seat for my flight! (Strangely you do that on SouthWest now??? Weird. 

I was asked to amplify some of the mass spec stuff that can sometimes get lost among the FACS and microscopy and inferior technologies like genomics/transcriptomics. 

Honestly, I have 6 different scSeq things circled on my calendar, as well as some microscopy stuff. 

There is still mass spec there! 

Self-serving, the session I designed is the first one that pops up (there will clearly be lightning talks and posters) but the first proteomics session got the super ideal timeslot of 4:30pm on Tuesday! 

I hope that we'll make these available via Webinar as well. Working on it. ABRF had a blanket deal with one of the big webinar companies and the squeeky wheel seems to get the limited time slots).

There will be a talk on technology from a vendor that I haven't gotten to see yet as well! 

Wednesday is a super cool session that I literally changed my flight to be able to catch. 

Artificial intelligence in proteomics? Let's goooooooooooooooo! 

Oh yeah...and there is Metabolomics! (Insert vomiting sounds here)!  

Got 3 minutes? Vote for TalusBio (and proteomics) to win a cool award!

TaluBio is this funky spinout company that has stolen some of the best and brightest young people in our field and is out doing crazy cool proteomics drug discovery work on rare diseases.

You probably knew that, but did you know that they're up for some big award against all sorts of funky startups? It's a funny list to go down, like one company makes autonomous tractors. Hopefully.... by... developing their own algorithms and not using these... no one wants what happened to Jeremy Renner (he got better). 

You can go to GeekyWire and check it out (and vote for TalusBio) here! 

SpectroPipeR - Clean up those .SNE files with this R stuff!

 

I can't wait to try this and compare it to DIA-Analyst! 

Actually tell if it is an ADP-r or a PolyADP-r with insanely hard sample prep!

 

If you're ever feeling really good about your skills as a proteomic mass spectrometrist and need knocked down a peg, I strongly recommend going into the amazing field of ADP-Ribosylation. This amazing modification can be one ADPr or long chains of repeated ADPr. Sounds fun, right? 

Glycoproteomics is great because there are like 1-4 sugars for each mass and they can go on like 5 amino acids? I forget, and each sugar chain can break in like 2 or 3 places. 

ADPr cranks up the fun by each monomer fragmenting in 7 possible places, the mod can exist on 11 different amino acids and - again - each monomer has the exact same mass. 

Look at this awful thing (from this great JASMS paper from a couple years ago) 

Sign me up for citrullination on an Orbitrap (please don't) or phospho vs sulfation vs nitration on a TOF (for real, no, please don't) any day of the week. 

So Anthony and Isabel had this great idea of MAKING THIS HARDER. Because the way people do it is to cleave it down to a much smaller mod, so you know that there was an ADPr, but you don't know if it was one or a chain. 

It took a village - and maybe like 5 years (including pandemic times which we all know like only sorta counts) but Isabel actually pulled it off. 

To really get it to work - you need that ETD + HCD (EThcD?) thing that you can only get on Tribrids. And while the preliminary work on Chan-Hyun Lab Lumos(es) looked good, you can really see the benefits of the enhanced signal and improved fragmentation dynamics on the Eclipse in Ben Garcia's lab. This didn't make the manuscript - but - wow - does this stuff look better with the labile mod workflows in the newer MSFraggers and huge thanks to Dan and Alexey's team for extra help setting those processing things up. 

NIH BioArt has a Q Exactive!

 

If you're rapidly drafting some figures - Biorender can be super cool. But what if you don't have a license? NIH BioArt is adding stuff all the time! 

https://bioart.niaid.nih.gov/

Proof? There is no way that it had a Q Exactive when it first made my blog last year! I would have found it, but now it has it and more free/open art to use to really drive that dumb point home that you're trying to make! 

While you're there check out how much cool new stuff has been put on NIH 3D! 

https://3d.nih.gov/

Mandatory reminder that these government resources are probably at risk right now. I mean... we should be concerned about how we will do science if PubMed is no longer a thing....

EasyPubPlot - Super simple amazing customizable plots in like 90 seconds!

 

Where was this when I was stinking up the NIH submission system with my crappy grant applications? 

You want a 110 pt font axis? You get a 100 pt font axis! 

Introducing EasyPubPlot! 

And I'm not messing around guys. This isn't easy for someone who knows off the top of their head what version of R is on their computer. I'm not talking easy for someone who knows the difference between a .txt and a .tsv and a .csv that only opens right in Notepad++, not NotePad+ or NotePad-

I want to take the vomit inducing plots from SpectroNaut and make a plot that is not vomit inducing? 

I go here. 

https://pharmaco-omicslab.shinyapps.io/EasyPubPlot/

I put in a .csv that contains my protein, my fold change and my p-value and -BOOOM volcano plot! 

Is it going to impress your bioinformatics friend carries a shoebox of hard drives with them wherever they go? No. Because he/she/they can make a volcano plot some other magical way that I can not. 

Did I make the nicest proteomics volcano plot I've ever made in my life in about 90 seconds? HELL yeah I did and then I cranked the fonts up to 110 and turned off the legends and swapped the colors around. I DID THIS WITHOUT LEARNING ANYTHING. AT ALL. 100.00% of my brain was focused on a ridiculously good bibimbap. 

For real, it's like they took everything I need to make plots and simplified it down to just those things. It's like what the Red Elephant did for manual peptide sequencing. Or simplifying Prosit down to actually what I need from Prosit the way EL FRAGMENTADOR does. 

I've been paying for and using GraphPad for 5 years and I can't make something in it that isn't hideous without 4 hours of work. It's eventually okay, but - man does it start out dumb every time you put data in it. EasyPubPlot just makes nice plots! 

It does more stuff than Volcano, too! This is just what I'm most excited about. 

The heatmap feature is nice, but if you need a heatmap the Broad's Morpheus toolkit is probably a little nicer and it has embedded statistics. But if you just want to make a nice publication ready bubble plot or box plot - the tutorials are ON POINT. 

Is there a problem? 

The pub is short for "publish" but could you make a publication ready volcano plot at a pub? There is only one way to find out, probably. 

Too lazy to read? Navigate NCI resources with DrBioRight 2.0!

 

Just like everyone else, I'm absolutely fucking thrilled to see Artificial Intelligence showing up all over the place. A lot of commercial websites now have slow new AI functions that are essentially "ctrl+f" in your web browser - the kind of improvement is assume Elon Musk and his fans are most excited about. 

(Another amazing new success from Mr. Musk this week. Add that with the fact that they're  

now leasing office space for HHS bioinformaticians to log into AWS to do their work...cause that's a cost savings over them logging into AWS from spaces that cost the HHS $0! .... Though my favorite thing this week is the stated attempt to put AI into all government databases.. ..which...run...on.. .COBOL.... if you aren't familiar, when I took Fortran 77 in highschool (the 77 was the year that version of the language was finalized - which is a good summary of Appalachian high schools in the 90s) my teacher would make fun of COBOL being old and useless. Totally going to seamlessly integrate with AI. No worries there. 

While I'm ranting you can absolutely buy this at BestBuy right now. Just in case your "smart washer" from 2018 that is the single worst appliance you've ever owned in your entire life needs downgraded for 5x what a non-smart washer/drier that works costs! 

With that lead in! 

Introducing Dr BioRight 2.0! A large language model to search NCI data! 

If you're thinking something like "oh no, did peer reviewers see the words 'large language models and multi-omics' in the abstract and just accept it without ever looking at it?" you're probably a cynical jerk. Geez.... I mean, that's exactly what I thought, and that's why I spent my morning trying to see if this LLM could actually do things (other than generate "Internal Server" errors, which, to be fair, it generates an awful lot of) 

Short summary? There is some value here, I think. Particularly if you're not a big fan of reading, but you're patient enough to ask an algorithm to dig through some results for you, and you aren't patient enough to dig through said results yourself. 

For a bad example - I went to a pancreatic cancer dataset and I asked Dr. BioRight how often KRAS mutations show up in the cohort. Since it's the most mutated gene in that dataset, I figured it would skim the abstract and give me the answer. It didn't. ChatGPT400 or whatever it's called now will do that for you, that's not why you want to use this tool. 

I got frustrated, saved this blog post in my "do not push the publish button, Ben" folder and moved on. I went back because it popped up in my newsfeed and then I grabbed a random dataset and started asking it stuff about the source data in the study.

 And this is where things get really interesting. In this study with a 65 patient cohort I just asked what the most commonly mutated genes were. It appears to generate R commands and run them?  

That's actually super legit! Slow....but it really sounds impressive. Now - I'm not checking to see if this is actually right, but you can see where this might be an asset.

You don't go to an AI to make art for you because you know how to make art. You don't ask ChatGPT to write something for you if you want it written well and accurately. And you don't want to ask Dr. BioRight 2.0 about a cancer study that you reanalyzed yourself and contributed to a paper on. But if you are interested in skimming publicly available data for stuff you want to dig into depth on, it is faster than pulling the manuscript source data, figuring out which table is the one you want, and looking for it yourself. And that can probably be of use at times. 

Multi-omics of coal miner's exposed to 10+ years of coal dust....

 

I was really torn on using the above .gif because I come from a long line of people who have died either in coal mines or thanks to what they inhaled down there. Unfortunately, all of my cousins that are under the ground somewhere in WV bathe in the political disinformation kool-aid to the point that I can't even talk to them, so we're going to have some fun with this important new study! 

If you think being a coal miner in West Virginia is dangerous just because the governor of the state made his billions of dollars running the least safe mines in the USA and never paying his legal fees - you'd be right. But it's a super dangerous thing to do anywhere in the world and there is always some piece of dog shit who is willing to save $1 by making it a little less safe for you. 

This study found a cohort with the following requirements - you had to be over 40 and you had to have spent more than 10 years breathing coal dust. How'd they know they weren't lying about being down there 10 years? Well...most of them had easily diagnose-able pneumoconiosis. Which, yes, I had to copy and paste. They did have to exclude people who had lung cancer... ugh...so if you know Zoolander, maybe the funniest movie that ever happened that doesn't have Will Ferrell as the lead, you know what .gif I should use next and I probably shouldn't.

They didn't go after plasma - they went after BALF (no, I can't spell it) which is a fluid in the lung that is not fun to get from people. I hope they compensated these volunteers very very well. 

They FASP'ed the BALF (man, if you are thinking of FASP'ing human samples, I'll legit send you S-traps they're like $5 a sample, particularly if you cut someone's lung while they were under anaesthesia) for proteomics using a TIMSTOF Pro and they did the metabolomics on an Exploris 120. Everything looks pretty standard. MaxQuant for the proteomics data and MetaboAnalyst for metabolomics. 

I can't exactly follow where the ELISA came in here in the study - like what materials and why when they had proteomics on the BALF. I figured we'd be doing ELISA on cool blood targets, and maybe that is what they did? Oh - they did ELISA on some BALFs from these patients and an unanalyzed (by LCMS cohort). I think.

Hey - I wonder if the proteomic or metabolomic alterations of breathing coal dust for 10 years is obvious? Oh.

Yeah....it might be obvious.....

I'm not sure what the authors are planning to do with these data. I mean, you could probably put together a powerpoint presentation on - inhaling coal dust for 10 years alters your lungs on a basic molecular level? 

Completely unrelated - 

MaxQuant updates for isobaric quan - TMTPro/MBR and no reference channels(?)!!

 

Oh yeah! Can't wait to try this out! 

Most interesting, maybe is that in the application of isobaric match between runs and new stats with/without reference normalization - it looks like pooled reference channels between plexes may not be necessary. The link above is my kluged together solution for matching between TMT plexes without reference/pools using high abundance "boring" proteins and I largely put that post there so I could find those same data and quickly compare the two. 

Something I hoped to get a good answer to from the paper is whether the TMT MBR was working well for non-Orbitrap data. I used it for a relatively small TIMSTOF TMT set from early 2021 and it did appear to decrease missing values, but n=1 and I could have just got one of my settings off between runs 1 and 2, but it could be a gamechanger for some of the stuff we're working on. You want to hear that it is working before you ask MaxQuant to load up 600 Bruker .d files, though, but sometimes you just have to YOLO and apologize to the other users of that PC later.