RyboDyn Awarded $1.3M DOW Grant to Advance Novel Antibody Therapies Against Cryptic Lung Cancer Targets

RyboDyn, Inc. (搜索)搜索该词条, a San Diego-based biotechnology company decoding the "dark proteome," has been awarded a $1.3 million grant from the U.S. Department of War (DOW) to advance preclinical development of two novel antibody-based therapies for lung cancer (搜索)搜索该疾病. The award, announced July 9, 2026, will support the company's antibody-drug conjugate查看药品详情 (ADC) and T-cell engager (搜索)搜索该药品 (TCE) programs targeting two previously undiscovered cell-surface proteins identified directly from patient tumors through CypherAtlas™, RyboDyn's integrated dark transcriptome and proteome atlas.

The award was made through the Congressionally Directed Medical Research Programs (CDMRP) Peer Reviewed Cancer Research Program (PRCRP) Impact Award, a highly competitive program supporting innovative approaches with the potential to significantly improve outcomes for cancer patients. The funding follows RyboDyn's recent Seed financing and continued expansion of CypherAtlas™, now one of the largest integrated resources for cryptic cancer biology.

A Target Problem, Not a Molecule Problem

Despite advances in AI, protein engineering, and antibody discovery, nearly 90% of oncology drugs entering clinical trials still fail to reach approval. In many cases, the limiting factor is not the quality of the therapeutic molecule but the biology it is designed to target. The industry continues to pursue the same relatively small set of well-characterized targets, which are often present in only a subset of patients or also expressed in healthy tissues—limiting efficacy, increasing toxicity, and leaving many patients without effective treatment options.

Of roughly 13,600 drug-target pairs in the global preclinical and clinical pipeline, a quarter rely on just 38 protein targets. The rate at which new targets enter the development pipeline has fallen from around one hundred a year a decade ago to roughly thirty in 2024. RyboDyn approaches drug discovery in reverse: rather than beginning with known biology, the company starts with patients, mining thousands of tumors to identify previously hidden proteins that recur across large patient populations while remaining absent from healthy tissue.

An Unprecedented Atlas of Hidden Biology

At the core of RyboDyn's discovery engine is RyboCypher™, which combines deep sequencing of non-canonical RNAs, proteomic validation, and foundational AI models to identify disease-specific therapeutic targets that conventional approaches cannot detect. Each discovery is integrated into CypherAtlas™, the company's continuously expanding atlas of the dark transcriptome and dark proteome, now built from more than 2,000 patient samples spanning 12 oncology indications.

In a preprint posted to bioRxiv, the company describes what it calls a cryptic human proteome—tens of thousands of peptides that reference annotations never captured. Applied to lung and colorectal tumors, matched healthy tissue, and cancer cell lines, RyboCypher resolved roughly 6.6 million dark RNA loci, more than 97% of which are absent from existing non-coding RNA databases. Those transcripts yielded about 16 million candidate open reading frames. Searching those predictions against roughly half a billion mass spectra drawn from 2,229 patient samples returned about 80,000 cryptic peptides at a false discovery rate below 1%, of which roughly 10,000 are cancer-associated or cancer-upregulated.

Today, CypherAtlas contains more than 6 million conserved, previously uncharacterized dark RNAs, over 80,000 cryptic peptides empirically identified by mass spectrometry, and approximately 10,000 cancer-specific peptides with therapeutic potential. Compared against microprotein and peptide catalogs published in Nature earlier this year, the overlap is close to nil.

Validating the Biology

The cryptic loci cluster into protein classes drug hunters have chased for decades—deubiquitinases, E3 ligases, and transcription factors in cellular proteomics, and cell adhesion molecules, transporters, and receptors in membrane preparations.

"Oncology is overdue for another checkpoint-inhibitor moment," said Corey Dambacher, president and co-founder of RyboDyn and senior author of the study. "That doesn't come from finding a different way to go after the same three dozen targets. We can really only accomplish that by discovering new biology, and RyboCypher is built to find it."

To address concerns that a search space of 16 million candidates will produce noise, RyboDyn uses the ESM-2 protein language model to measure how surprising a sequence looks to a model trained on known proteins. The score ran higher for cryptic proteins than for the canonical reference set, with the elevation concentrated almost entirely in the novel regions absent from any annotation. The sequences still sat well below shuffled and random controls, and structure prediction largely returned confidently folded models.

"These proteins were, in the most literal sense, perplexing to the best protein language models available," said Imad Ajjawi, chief executive and co-founder. "AI could not have guessed this biology existed, because it was never in the training data. Yet the sequences are nowhere near random. As proteins they're statistically novel, but structurally sound."

Proof of Concept: cYBX1

The preprint's proof of concept is cYBX1, a cryptic protein expressed from the YBX1 (搜索)搜索该靶点 locus. Canonical YBX1 is a well-known oncogenic regulator that has proven stubbornly undruggable. The cryptic version gives rise to a tumor-restricted peptide-MHC complex on the cell surface. TCR-mimic antibodies raised against it bound at 626 picomolar and showed no measurable binding to a nearest-neighbor peptide differing by three amino acids. One antibody recognized the target across three HLA-A*02 isoforms, which the company estimates raises the addressable population above forty-five percent in Western European and North American populations. Formatted as an antibody-drug conjugate查看药品详情, the lead killed tumor cells in vitro.

Prevalence Data and Clinical Significance

RyboDyn's top-ranked cell-surface candidate is upregulated roughly 7.5-fold in approximately 80% of the lung squamous cell carcinoma (搜索)搜索该疾病 patients assessed (a cohort of over 100), with significant tumor upregulation also seen in clear cell renal cell carcinoma (搜索)搜索该疾病 (ccRCC) and pancreatic ductal adenocarcinoma (搜索)搜索该疾病 (PDAC). By comparison, a well-known blockbuster target like HER2 (搜索)搜索该靶点 is actionable in only 15–20% of breast cancers.

"This work establishes the principles and validates the concepts RyboDyn is pursuing," said Gordon B. Mills, director of the SMMART trials at the Knight Cancer Institute at Oregon Health and Science University.

Why Lung Cancer Remains a DOW Priority

Lung cancer (搜索)搜索该疾病 remains the leading cause of cancer death in the United States and disproportionately affects military service members and veterans. Exposure to burn pits, asbestos, diesel exhaust, Agent Orange, and other occupational hazards, together with historically higher smoking prevalence, has contributed to substantially elevated lung cancer risk within veteran populations. Approximately 8,000 veterans are diagnosed with lung cancer each year. By expanding the pool of novel therapeutic targets, RyboDyn's approach aligns closely with the DOW's mission to improve treatment options for service members, veterans, and the broader cancer community.

Under the DOW award, RyboDyn will generate therapeutic candidates against both prioritized targets, characterize their biological activity, and advance the programs through in vivo proof-of-concept studies. The prioritized targets emerged as two of the most prevalent cell-surface targets identified in lung cancer (搜索)搜索该疾病, making them particularly well suited for antibody-drug conjugate查看药品详情 and T-cell engager (搜索)搜索该药品 development.

Limitations and Outlook

The usual qualifiers apply. The work is a preprint and has not been peer reviewed, the sequencing chemistry behind RyboCypher is licensed from Oregon Health and Science University and remains undisclosed, and the tumor killing is in vitro with no animal data reported. What RyboDyn has done is move the hard part: finding new targets used to be the scarce step, and the company now claims one to five high-value druggable candidates per indication. Converting any one of them into an asset is the older problem, and it is the one the field will judge RyboDyn on.

RyboDyn is built on intellectual property licensed from Oregon Health & Science University Knight Cancer Institute (搜索)搜索该词条, based at Lilly Gateway Labs in San Diego, and a member of Lilly's AI TuneLabs consortium and NVIDIA's Inception Program. The company maintains a publicly disclosed collaboration with Moffitt Cancer Center查看公司主页 and is backed by Genedant, SeaX Ventures, SOSV, Swell VC, Massive Tech Ventures, and P2V.