george demetri

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george demetri

george demetri

@DrSarcoma

Physician/investigator/educator/academic and industrial collaborator. Prof, @Dana-Farber and Ludwig Center @Harvard Medical School.All tweets my personal views.

Boston, MA Katılım Mayıs 2012
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george demetri
george demetri@DrSarcoma·
Keynote speaker Prof Alice Shaw of @DanaFarber reviewing the wave of RAS inhibitors and pointing the way forward at today’s TREX meeting with @GustaveRoussy
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george demetri
george demetri@DrSarcoma·
Thanks to nonprofit Institut Servier for helping build this collaboration!!
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Dr. Anthony Letai
Dr. Anthony Letai@NCIDirector·
We celebrate the life of Nobel laureate J. Michael Bishop, whose discovery of oncogenes transformed our understanding of cancer. At @theNCI, we see his legacy in today’s progress—advancing research based on oncogenes that continues to change and save lives nobelprize.org/prizes/medicin…
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george demetri
george demetri@DrSarcoma·
@jaybradner He spoke so highly of you, Jay. He was very proud of your work together that led to Pluvicto. May his memory be both a blessing and an inspiration.
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Jay Bradner, M.D.
Jay Bradner, M.D.@jaybradner·
Learned today of the passing of Phil Low, a drug hunter, scholar, educator and just lovely man. His work to target cancer cells via folic acid receptors, among so many other creative ideas, were a source of inspiration so many years ago. It was an honor to work with him on PSMA radioligand therapy, and to develop his inventions further to real medicines while at Novartis. Phil was a dynamo in his Purdue laboratory, surrounded by adoring trainees, at a whiteboard or pounding through 80 packed Powerpoint slides in 30 breathless minutes. I am glad and better to have known you, Phil. Your memory is a blessing. linkedin.com/posts/purdue-u…
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Sid Sijbrandij
Sid Sijbrandij@sytses·
I’m going Founder Mode on my cancer. Below is Elliot Hershberg’s article about my cancer journey. It gave language to something I’d been doing instinctively over the past year: managing my health in Founder Mode. Manager mode assumes that existing systems will surface the best options. When I was first diagnosed with cancer in 2022, I delegated the crucial analyses and decisions about my care to others. In late 2024, when my cancer reappeared and my doctors told me I had exhausted the standard of care and there were no trials for my situation, I realized that assumption might, quite literally, kill me. Founder Mode was my only option. Founder Mode meant going deep on every diagnostic and treatment option. It meant assembling a team of physicians and scientists to work from first principles to understand what was possible beyond standard protocols. Together, we paved new roads to access the very cutting edge of science and technology. Today, thanks to the efforts of many people around the world and the support of my wife Karen, I currently have no evidence of disease. But my fight with cancer is far from over. My team and I continue to develop treatments and strategies in case it returns. More importantly, I now understand firsthand the challenges patients face in order to secure their own data and necessary treatments, particularly personalized medicines. I increasingly see my role as removing structural barriers—breaking down walls that prevent data, treatments, and technologies from flowing where they’re needed. One of the core principles of the first company I founded, GitLab, was radical transparency, and it’s a principle I am bringing to my cancer care. To that end, I am going to be sharing more about my experiences, my treatments, my data, and what I am building to make the path that I’ve been on easier for others to follow. Please subscribe to my mailing list on sytse.com to stay updated. Lastly, I want to thank those who have been on this journey with me. There have been too many to all thank here but I appreciate every one of you. I did want to mention Jacob Stern, Alfredo Gonzalez, and Jeremiah Wala; the amazing teams at Private Health Management (shoutout to Jenn and Eva) and Willy Hoos and Pathfinder Oncology; Nima Afshar and Private Medical; Sant Chawla and the Sarcoma Oncology Center; John Connolly and his team at the Parker Institute; Will Hudson at Baylor College of Medicine; Kamil Slowikowski for his work on osteosarc.com; and Jeff Tsao, Will Gibson, Ali Samiei, Scott McConnell and the rest of the team at the Briger Foundation for Oncology Research.
Elliot Hershberg@ElliotHershberg

Going Founder Mode On Cancer centuryofbio.com/p/sid Sid Sijbrandij is a generational founder. He founded and led GitLab, one of the largest remote companies in the world, from idea-stage startup to NASDAQ-listed software giant. But in 2022, a six centimeter mass growing from his upper spine threatened to end all of that. He had cancer. What happened next is nothing short of remarkable. Sid went founder mode on his care journey. In the years since, he's deployed cutting-edge genomics to profile his disease. Based on this data, he's developed a growing armamentarium of personalized therapies. As a result, his disease is now undetectable. A simplistic version of this story could be, “Wow! A brilliant billionaire seemingly cured his cancer. Good for him!” But as I’ve gotten to know Sid, it’s become abundantly clear to me that there is more to the story than that. In an in-depth profile for The Century of Biology, I explore Sid's journey and what this might mean for the future of cancer care.

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Eric Topol
Eric Topol@EricTopol·
A randomized trial of using an LLM by primary care physicians for referral to specialists (vs no AI) provided substantial improvements in workflow, patient experience and less test ordering @NatureMedicine nature.com/articles/s4159…
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george demetri
george demetri@DrSarcoma·
Research matters to drive advances against cancers. I just committed to ride the Pan-Mass Challenge, where 100% of funds raised support key research at Dana-Farber Cancer Institute. Click here to support my ride! donate.pmc.org/GD0026 #PMC2026 via @panmass
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JimiRocks
JimiRocks@Jimi_Stella·
When Dylan says your covers of his songs are better than his original you know you’re pretty good Respect #DeadHeadLife
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george demetri
george demetri@DrSarcoma·
As Rick Klausner said, “Cancers use every trick of evolution to stay alive.” Betcha a large coffee that hard-to-treat cancers adopt this trick and more. Research opens new doors to therapies.
Niko McCarty.@NikoMcCarty

This paper is wild. After 3 rounds of directed evolution, they converted a DNA polymerase into an enzyme that can do: - RNA synthesis - Reverse transcription - Synthesis of "unnatural" nucleotides - Synthesis of DNA-RNA chimeras One of the best papers I’ve read recently. For context: In nature, it is DNA polymerase that takes a DNA sequence as a template and then copies it. These enzymes are crucial in replicating the genome for cell division, and they are EXTREMELY specific for DNA over RNA. This is key because RNA nucleotides are present in the cell at concentrations ~100x higher than DNA nucleotides, so the enzyme has evolved clever strategies to select one over the other. RNA polymerases, for comparison, are the enzymes that take a DNA sequence as template and then convert it into RNA. They are involved in gene expression, for example. To convert a DNA polymerase into an RNA polymerase (and all the other functions I mentioned earlier), the authors did a fairly straightforward directed evolution experiment. First, they took four DNA polymerase enzymes belonging to various archaea. These DNA polymerases don’t check for DNA vs. RNA as stringently as other types of cells, so they’re a good starting point to evolve RNA polymerases. The authors inserted some targeted mutations into these enzymes, based on known mutations in the literature. For example, they swapped the amino acid at position 409 for a smaller amino acid, thus removing a “gate” that keeps RNA building blocks from entering the enzyme. Next, they took the four genes encoding these DNA polymerases and cut them up into 12 segments each. They randomly stitched these 12 segments together — from the four different genes — to build millions of unique variants. Each shuffled gene was inserted into an E. coli cell. Then, they grew up these cells (each carrying a unique polymerase) and put them into microfluidic droplets. A device isolates each droplet, lyses the cell open, and releases the polymerase. The droplet also contains RNA building blocks and a DNA template, encoding a fluorescent reporter. If the polymerase begins synthesizing RNA, it will produce a detectable signal. They screened about 100 million droplets in 10 hours of work, searching for those with a signal. For each well that yields a fluorescent signal, the researchers isolated the DNA and sequenced it to figure out which polymerase it was. They repeated this 3x times, finally isolating a really excellent RNA polymerase variant which they called "C28." C28 has 39 mutations compared to the wildtype enzymes. It incorporates about 3.3 nucleotides of RNA per second, with 99.8% fidelity. The crazy thing is that this enzyme can also copy DNA or RNA templates back into DNA (reverse transcription), or use chimeric DNA-RNA molecules as a template and amplify them. It is just a super versatile polymerase that can act on DNA, RNA, or modified nucleotides, to build just about anything.

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Lost In Film
Lost In Film@LostInFilm·
Fritz Lang’s 'Metropolis' premiered in Berlin on this day in 1927. A landmark of silent cinema and science fiction, famous for its mesmerizing visuals and monumental scale. The film, set in 2026, imagines a hyper-industrialized future split between an elite ruling class and oppressed workers underground.
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Empire State Building
Empire State Building@EmpireStateBldg·
Tonight we will shine in tie-dye to honor the life and legacy of Bob Weir @GratefulDead
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Bauhaus Movement
Bauhaus Movement@BauhausMovement·
🎉 Born on this day, 1879. Paul Klee would have turned 146 today. “Art does not reproduce what is visible, but makes it visible.” #PaulKlee #Art #Bauhaus
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Alex Prompter
Alex Prompter@alex_prompter·
This paper from Harvard and MIT quietly answers the most important AI question nobody benchmarks properly: Can LLMs actually discover science, or are they just good at talking about it? The paper is called “Evaluating Large Language Models in Scientific Discovery”, and instead of asking models trivia questions, it tests something much harder: Can models form hypotheses, design experiments, interpret results, and update beliefs like real scientists? Here’s what the authors did differently 👇 • They evaluate LLMs across the full discovery loop hypothesis → experiment → observation → revision • Tasks span biology, chemistry, and physics, not toy puzzles • Models must work with incomplete data, noisy results, and false leads • Success is measured by scientific progress, not fluency or confidence What they found is sobering. LLMs are decent at suggesting hypotheses, but brittle at everything that follows. ✓ They overfit to surface patterns ✓ They struggle to abandon bad hypotheses even when evidence contradicts them ✓ They confuse correlation for causation ✓ They hallucinate explanations when experiments fail ✓ They optimize for plausibility, not truth Most striking result: `High benchmark scores do not correlate with scientific discovery ability.` Some top models that dominate standard reasoning tests completely fail when forced to run iterative experiments and update theories. Why this matters: Real science is not one-shot reasoning. It’s feedback, failure, revision, and restraint. LLMs today: • Talk like scientists • Write like scientists • But don’t think like scientists yet The paper’s core takeaway: Scientific intelligence is not language intelligence. It requires memory, hypothesis tracking, causal reasoning, and the ability to say “I was wrong.” Until models can reliably do that, claims about “AI scientists” are mostly premature. This paper doesn’t hype AI. It defines the gap we still need to close. And that’s exactly why it’s important.
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