OneRNA

There are so many ways to kill cancer cells but using only one drug is not one of them. The way we historically developed and approved drugs is antiquated and the output is low with billions of dollars wasted. 

Relying on single biomarkers has been practiced for four decades, resulting in drug approvals of 5% of drugs entering the clinic and resulting in response rates of 50% in early-stage cancers and 20% in late-stage cancers. 

Today, 50%-80% of cancer patients who do not respond to standard of care have access to DNA panel sequencing to repurpose approved drugs finding 1 drug in 40-80% of the patients and improving outcomes for 8-16% of patients.

Analyzing all RNAs enables truly individualized treatment by 1) repurposing existing drugs and 2) Individualize mRNA vaccines or other RNA therapeutics. 

However, RNA is much harder to analyze than DNA because the assay must be quantitative, and RNA is not stable in real clinical samples. 

Individualized mRNA vaccines can boost response to immune therapy, today they take 9 weeks to produce. Translating RNA Code to RNA Therapeutics in 1 week could open the door for truly individualized RNA Therapeutics and AI enable the delivery of care at a scale never seen before.

We have solved the key issues with bringing RNA into the clinical as a standard assay and have also solved the translational issue with the time it takes to translate RNA code to RNA therapeutics which opens up for the possibility to truly individualize treatment and combining it with the right drug to sensitize the tumor, prevent escape or take the breaks off the immune system. 

We got to where we are for mostly grants ($6.8M) and research revenue as well as the support from angel investors ($1.7M) 

We have developed and clinically validated an RNA Platform that can analyze and rapidly translate RNA code to individualized treatments and novel RNA therapeutics.

Today individualized mRNA vaccines from Moderna and BioNtech both demonstrated that individualized mRNA vaccines can boost response to immune therapy - however, it takes 9 weeks to manufacture. What if we can bring that down to 1 week?

We are commercial stage with regulatory approvals established (CLIA) to analyze tumor samples.

The platform is a clinically validated platform that integrates the following technologies:

1) Chemistry that enables quantitative RNA-seq in real clinical samples such as paraffin-embedded tissue samples (FFPE) and liquid biopsy samples where the RNA is destroyed. 

2) Identification of abnormal gene expression by comparing one patient's sample to a database of normal samples eliminating the need for a second biopsy. 

3) Actionable report matching abnormally expressed genes to a proprietary database of all approved drug targets, ligands, and biomarkers.

The above RNA Platform called OneRNA® is fully validated under CLIA in breast and ovarian cancer and tested in colon, brain, and lung cancer. We typically identify 5 FDA-approved drugs in 100% of patients with the potential for better outcomes for most. 

The following is under development: 

4) Novel proprietary chemistry that translate RNA Code into RNA Therapeutics in 1 week.

5) Methods to from a simple blood sample enrich for live B and T cells - and other cells as well - to analyze malignant cells and immune response as well as testing existing drugs and individualized RNA therapeutics prior to administration.

The above capabilities greatly accelerate innovation cycles.

In the future, the abnormal quantitative RNA data can be integrated with electronic medical records (EMR) to produce RNA algorithms (AI) for lack of response to standard-of-care, & recurrence. Those algorithms can reduce cost of care and suffering by enabling patients to opt out of standard of care sooner. 

RNA algorithms have already been implemented into standard of care using less powerful platforms such as PCR eg OncoTypeDx enable early stage breast cancer patients to opt out of chemo. 

We built this platform to serve the 50-80% of cancer patients that are not responding to the current static standard of care model and it's our ambition to create RNA algorithms based on quantitative RNA measurements in the tumor tissue based on ALL mRNA’s rather than a few, so that these patients can opt out of standard of standard of care and get access to other treatments sooner - reducing suffering and cost in our healthcare system. 

Quantitative RNA algorithms using PCR and other binary technologies has been implemented in the clinic in cancer as well in other indications. 

OneRNA® can accelerate this process in novel business models linking OneRNA® data to patient-derived data (e.g., treatment, response, diet, exercise, sleep) through EMR and other devices. 

The OneRNA® platform has vast utility from designing drugs and clinical studies to identifying patients, monitoring response and relapse. Because we are analyzing all RNAs, the platform is exponentially scalable and disease-agnostic. Thus the OneRNA® platform can also be leveraged in partnership with biotech and pharma: 

1) Design and de-risk clinical studies by identifying the best companion drug to pair the Rx drug with and design an adaptive study to test that eg there are multiple checkpoint inhibitors approved but which one is in play in the individual patient and how frequently is it overexpressed in the target indication in combination with the target for the drug. 

2) Enable top oncology medical centers to perform the test in-house (OneRNA kit +OneRNACloud) and generate the AI-supported response algorithms. These hospitals have already developed DNA sequencing testing in-house. Adding OneRNA can further identify patients who qualify for specific clinical studies and enable the development of novel RNA AI algorithms faster by combining the quantitative actionable RNA data with clinical data such as treatment, scanning, and outcome data. 

3) Service community hospital systems under CLIA which is where 80% of the cancer patients are being treated. That enables patients and community centers to enroll patients into decentralized clinical studies broadening patients' access to trials.  

Genomic Expression was founded by Gitte and Morten Pedersen, who had the unfortunate experience of losing several family members to cancer. Because they are from a scientific family of entrepreneurs and both had degrees in life sciences, they decided to start a company focusing on what they believed from the beginning the most informative biomarker molecule = RNA, and then leverage Next Generation Sequencing to better inform treatment selection. They both now immigrated from Denmark to the USA and the company has US HQ. They subsequently raised non-dilutive capital ($6.8M)  to advance the technologies from concept to product and regulatory approvals. 

Today they remain passionate about transforming the way cancer patients are treated and because of their upbringing, they are also passionate about providing healthcare for all.

During COVID-19 the company pivoted early to assist in the lack of testing capacity and filed their own FDA EUA. They validated in saliva as well as NP swabs to enable at-home self-collection of samples, however, the FDA perceived saliva an unusual sample type and requested additional validation - this was March 2020. They then teamed up with Yale/Saliva Direct to prove that saliva outperforms NP (and AN swabs) swabs in collaboration with a number of other smaller CLIA labs across 41 states. Today this network comprises 200 CLIA labs that are dedicated to servicing their communities for the benefit of public health. 

To service patients in remote areas, they built out a cloud-based HIPAA/CLIA compliment backend solution tracking everything from order, payment to shipping of sample collection kit and the samples' arrival to the lab and processing to delivery of a report simultaneously to the customer, patient, and doctor. This infrastructure can connect to EMR and also service patients in their homes of self-collection liquid biopsies which democratize access to testing. They validated BRCA in saliva to enable BRCA testing of patients at home. Once a family member is diagnosed with BRCAmut the rest of the family needs to get tested too, however, that is not happening in the current system for multiple reasons and one of them is access to testing.

Furthermore, as a part of the latest grant with NCI in rare cancers, a large customer discovery project was concluded identifying multiple community hospitals that are interested in partnering with us to commercialize the OneRNA Platform. 

Gitte Pedersen: CEO former executive from Novo Nordisk. Developed and launched multiple products in multiple industries worldwide. Was advisor to the Danish Government and now the European Commission ESIR2 member. 

Morten Pedersen: CSO, PhD is the inventor of GEx’s foundational technologies with a very strong innovation track record from Chr Hansen. 

Tanya Kanigan: Advisor, post-doc from MIT, developed OpenArray®, and spun it out into BioTrove which was later sold to Life Technologies. Diagnostic Big Data AI.

Jesper Zeuthen: CMO, PhD is the co-founder of GeneMap and DanDrit. He established the Danish Cancer Society and is a pioneer in immune-oncology. 

Sugganth Daniel: Medical Director, MD, PhD, Foundation Medicine, Quest, Invitae, Illumina 

RNA as a biomarker is well established in the clinic using less powerful platforms such as PCR, however, RNA-seq has remained an R&D application due to 3 key issues 

1) RNA is not stable and is destroyed in real clinical samples such as formalin fixed paraffin embedded tissue samples (FFPE). The variable RNA sizes create bias in the dataset which makes the data very noisy. 

2) In order to identify aberrant genes the tumor expression RNA data has to be compared to a normal dataset and in the research a 2nd biopsy of normal tissue can be obtained from the patient, however getting another invasive procedure accepted in the clinic will create a lot of friction and double cost. We have shown that 2.1) Normal adjacent tissue is not normal and somewhere in between tumor and normal and therefore not a good comparator, and 2.2) Normal expression ranges of true normal tissue from people without any cancer diagnosis - or other disease - is a much more effective comparator 

3) Once the aberrant genes have been identified they have to be checked for false positive and negative as well as connected to drugs that interogates them as targets. We built a database and a cloud-based infrastructure to automate this process from sample acquisition to report 

Some RNA sequencing panels using ArcherDx chemistry focused on fusion detection which is only relevant in 5% of solid tumors (exited to Invitae for $1.4B). Research-grade RNA sequencing kits fail when analyzing paraffin-embedded samples (FFPE) and are neither quantitative nor able to translate data into clinical reports. Exome panels applied to RNA expression in FFPE are not quantitative nor reproducible. Liquid biopsy companies are focusing on ctDNA in blood. These assays do not detect early disease and may produce significant false positives. Using only DNA markers will miss cancers that do not harbor mutations.

We have clinically validated the OneRNA chemistry in combination with proprietary algorithms to eliminate bias and build databases of normal expression rages and drug databases to enable this platform in a clinical setting. Data published at SITC and NYAS, focused on validation in breast cancer first where expression-based biomarkers are implemented in standard of care using PCR and IHC. 

Treating patients with expensive toxic drugs that don't work is inhumane and drives up cost and suffering. Being able to improve outcomes for cancer patients is a goal for most governments as is the reduction of cost of care. 

Genomic Expression's vision and mission are aligned with those goals. 

Vision: To Save Lives and Make Healthcare Delivery more Effective and Equatable 

Cancer is responsible for 9.0 million deaths annually, second only to cardiovascular disease (CVD) (17.9 million deaths annually) as the leading cause of Non-communicable Diseases (NCD)  death globally. 

The burden of NCDs is expected to continue to increase in low- and middle-income countries. The estimated economic losses associated with premature deaths - many of which are due to NCDs - are expected to rise to about US$ 7 trillion in these countries over the next 15 years. 

While actions to curb CVDs (Communicable Diseases) deaths have been successful in many world regions, cancer control has been challenging due to heterogeneity in the etiology, natural history, and pathology of the many different diseases that comprise cancer; cancer, therefore, deserves special attention with respect to the SDGs. 

Being able to accelerate the development of more effective drugs where the COGS are in the low and the manufacturing simple and portable to low resource settings are going to enable access to new life-saving drugs in those settings.

It is also very likely that the genetic drivers of cancer in other parts of the world is different from cancer in Caucasians living a Western life style. We simply know too little, however, we do have data points that support that there are significant differences such as: ALK fusions are the main driver of lung cancer in female Asian non-smokers. There are most likely more cases of this that are undiscovered at this point. 

Cancer has been categorized as a pandemic - as income goes up, western lifestyles get adopted in other parts of the world so do the health consequences that come with it. 

Genomic Expression is committed to providing access to the OneRNA® platform in countries that can leverage it to a) improve existing treatment options and b) design their own RNA cancer therapeutics 

No single entity has all the data in-house and our key contribution is a highly accurate and quantitative RNA-seq platform that produces robust data that identifies and quantifies ALL mRNA in a tumor sample. 

Our ambition is to provide the platform to accurately quantify all mRNAs and then partner with providers that have established DNA sequencing in-house and are open to improving the diagnostic yield by adding the OneRNA® platform. 

To that end, we have had conversations with some of the largest integrated healthcare systems in the US of which 3 have expressed interest. Furthermore, we have had initial conversations with Genome England and the Danish Healthcare system. 

Finally, we have based on conversations with our existing collaborators in Breast (Rutgers), Ovarian (MD Anderson, Dana Faber, and Rutgers), and Folicular Lymphoma (Mount Sinai) conceptualized algorithms that are meaningful to the oncologists treating patients and such algorithms are typically binary - will patient respond to treatment A which is standard of care. 

What OneRNA® further provides is suggestions for alternatives to standard of care which provides both patients and oncologists with additional options. 

However in order to answer the question; which patients will then respond to alternative B, C, D, …….N, that question can only be answered by actually treating patients with the drug and recording their response. 

That is the aim of the NCI phase 2 study in Ovarian cancer. Back to the opening statement of this application, the solution will most likely be a combination of drugs. 

Designing such combinatorial studies is one of the NCI’s ambitions and they started COMBO MATCH. Using mutations alone which typically are present in a particular tumor type in the single digits makes designing such studies challenging e.g. if 10% of patients have mutation A and 10% have mutation B, that means the patients that have mutation A and B are 0.1>#span class="Apple-converted-space"###  which will make enrollment into a clinical study next to impossible. 

Because the penetration of specific aberrant actionable genes in any cancer we analyzed is in the high double digits, it's possible to use OneRNA® to design a combinatorial clinical study using two drugs and still capture +20% of a specific patient population. We designed such as study in triple-negative breast cancer in immune oncology and data is present in this poster https://bit.ly/SITC2021OneRNA 

In conclusion, binary algorithms that enable oncologists to make clinical decisions between multiple treatment options are in demand. 

However, in order to answer the question “Will the drug work in a specific patient” and should the drug be combined with other drugs, such a question can only be answered through clinical studies with 3rd party drugs. 

To that end, we started conversations with drug companies to explore a novel business model that enables them to donate the drug for phase 1/2. Such a business model could include royalties, milestones, and upfront payments. 

In order to produce unbiased algorithms, it's paramount that the clinical data is also curated and standardized. We were part of the NCI’s bridge-to-AI program and it's clear that diagnostic interpretations vary and so does clinical practice. Specifically testing in the community setting is lacking which means that patients are not getting access to even standard of care (reported by the Personal Medicine Coalition estimates that 50% of lung cancer patients are not tested according to NCCN guidelines). 

The latter is an issue because one of the risks is not including an ethnically diverse population. The algorithm has to be created by selecting the right inclusion criteria to address this and also working with clinical partners that service diverse communities. 

Creating robust algorithms using RNA is established and so is the methodology (training set and validation set). To get to a robust 1st dataset, at least 1,000 samples need to be selected and analyzed. Furthermore, after validation, such a data set has to be prospectively validated. 

That can take a long time eg OncotypeDx was validated over a decade in 10,000 samples before it was fully accepted by the oncologist. That is a very long and expensive road and this kind of project will most likely not be funded by a for-profit organization. 

We hope that OneRNA®  providing alternative options for patients and selecting between options and not against a single option will facilitate faster adoption. 

Finally, large-scale ownership of responder algorithms to the current standard of care could be owned by a not-for-profit. The alternative is the NCI which does provide access to de-personalized sequencing data eg the TCGA study. 

In the end, the entity that sponsors a large study will have control of the ownership. We are actively working with not-for-profits to ensure that everybody has access. Finally, we are working with FullSky partners to facilitate the not-for-profit option. 

Our goal is to validate OneRNA® by analyzing +10,000 samples in +5 indications to 

1. Create responder algorithms for standard of care in partnership with large integrated providers both in the USA and outside the USA (OneRNA® kit + OneRNACloud® solution) 
2. Repurpose existing drugs in individual patients in adaptive combinatorial clinical study starting in Ovarian cancer with the NCI grants and then expanding into other solid tumors and liquid tumors as well
3. Developed our rapid translation of aberrant RNA Code to RNA Therapeutics capabilities to design and manufacture under GMP individualized RNA Therapeutics which can be integrated into an arm of the study described in B

Furthermore, we plan to launch OneRNA® commercially now to assist patients running out of treatment options and start saving lives by focusing first on low mutation frequency tumors such as breast and ovarian. 

Our ambition is to enable immune therapy in low mutation frequency tumors in particular and that is also where DNA panels are less useful. 

We will need to raise a Series A of $20M to get to A and B. We already have CLIA certification however we need funding for sales and marketing as well as the outlined clinical studies. 

Re C) We will start this project in Follicular Lymphoma where we have proven we can enrich live B, T and other cells from blood and test the drugs identified using OneRNA® on those live cells and then design RNA Therapeutics that can be tested in combination with existing drugs. 

The key competitive advantage that we have is the OneRNA® data and the proprietary capability to rapidly manufacture the DNA template for RNA Therapeutics. Translation of DNA template to RNA is an enzymatic process that takes 1 hr. The DNA template can take 1 month to get to a 100% accurate construct in sufficient amounts. We can produce the template +1,000 bp in a matter of hrs in sufficient quantities to eliminate fermentation steps. 

Once we have demonstrated POC on a novel RNA Therapeutics in a substantial number of patient cells we will partner the further development of the drug(s) with pharma. 

We can also partner with pharma now to accelerate the translation and validation of their proprietary RNA Therapeutics in our real patient systems and datasets. 

Our end-goal is to dramatically accelerate the development of real cures in oncology and provide patients with life-saving treatment options now. 

Once that is on its way we can start migrating into auto-immune diseases where the set-up we created for liquid tumors is quickly adaptable. 

Finally, we aim to integrate our OneRNA® solution with EMR in the community setting to facilitate testing according to NCCN and access to OneRNA® analysis for all patients increasing enrollment into clinical studies for underserved communities.

Our team is composed of a Wetlab team and a Drylab team, the latter includes individuals with Bioinformatics and Programming backgrounds. We have engineered the platform to automate the OneRNA® process from order to report and have added all pipelines to AWS. The Wetlab team is currently 2 full time and the dry lab is distributed 2 full-time bioinformatics and 4 part-time specializing in particular solution pieces. Then the management is 100% of the founder team's time (Morten and Gitte Pedersen) and 3 part-time. 

We need to augment the team with CBO and sales and marketing team 

We pivoted when we got into our first large grant project in 2017 and prior to that, we were pretty much in stealth mode. Since 2017 = 6 years, we worked full time on the project. In 2020 we obtained CLIA certification and rapidly pivoted into COVID-19 testing. Since inception, we raised $6.8M in grants and generated a total of $4.5M in revenue, clinically validated BRCA, COVID-19 in saliva (FDA EUA), OneRNA® in breast, and partly ovarian and follicular lymphoma (no published data yet). We also built out a fully automated cloud-based back-end from order to report. 

Our management team consists of almost 50% women and our board has 3 women and 3 men. Thus from a gender perspective, we are diverse, however from an ethnic perspective we need to improve with only 1 person in the management team from a non-white background. On the employee group, it's 5 out of 10 that has a non-white background. 

We have a diversity and inclusion policy and are acutely aware of our own unconscious bias - sharing Dana Kanze's TED talk here because it is illuminating.

Our mission is to "Save Lives and Make Healthcare Delivery more Effective and Equitable". 

In order to be believable on the last word in that statement we need to add individuals who have personal experience representing underserved communities and that is our goal not only from an employee standpoint but also from an advisory board perspective. 

We are already operating and battle-tested during COVID-19 where we scaled to 5,000 tests/day and our plan is to continue to operate as a CLIA Lab, however, we will need additional funding to scale as grants do not cover sales and marketing. 

Part of the NCI grant was an I-CORP project to interview 100 customers in 8 weeks. That was incredibly illuminating and also supportive in terms of our solution. 

We are raising funds to scale by expanding our team with a sales and marketing group and the management team with a CBO. 

We also want to expand the boards.

The details of the OneRNA® AI validation plan have been described elsewhere, particularly in the impact section thus that will not be repeated here.

Furthermore links to the executive summary and investor decks are provided in the next section. 

We are already operating and plan to continue to operate as a CLIA Lab, however, we will need additional funding to scale as grants do not cover sales and marketing. 

Part of the NCI grant was an I-CORP project to interview 100 customers in 8 weeks. That was incredibly illuminating and also supportive in terms of our solution. 

We are raising funds to scale by expanding our team with a sales and marketing group and the management team with a CBO. 

We also want to expand the boards.

The details of the OneRNA® AI validation plan have been described elsewhere, particularly in the impact section thus that will not be repeated here.

Furthermore links to the executive summary and investor decks are provided in the next section. 

Our current monthly burn rate is $50K however we are generating income from various projects and grants in 2023 we estimate a total of ca $500K in revenue. We are raising a bridge of $1M in parallel with a $20M series A. Deck and an executive summary can be downloaded here (Executive Summary, $1M Bridge deck, $20M Series A Deck). 

The key to financial sustainability is to raise funds to translate the leads we are generating to paying customers which means that in addition to the wetlab and drylab team, we need to establish a full-time sales and marketing team. 

We engineered the OneRNA® platform for scale and speed and our cost is less than $300/sample whereas the existing payor code for patients running out of treatment options is $3,000/test. 

Our business models are de-risked:

1. Partner with Pharma and Biotech companies to design and de-risk clinical studies 
2. Enable top oncology medical centers to perform the test in-house (OneRNA kit +OneRNACloud) 
3. Service community hospital systems (80% of cancer market) enabling enrollment of patients into decentralized clinical studies. 

Today, less than 1/3 of all clinical programs are using a biomarker adding significant risk and cost to the study. Using one biomarker can potentially reduce the cost of a study by up to $1B by reducing the trial size, timeline and de-risking the study. Because OneRNA® is analyzing all mRNAs, the assay becomes an algorithm on the platform that can be run at the right clinical decision point. This concept is portable to indications beyond cancer. RNA expression algorithms have been discovered and validated in most diseases. OneRNA® can further: 1) Expand the label and IP protection for existing drugs 2) Design, de-risk, and accelerate clinical studies 3) Leverage at-home collected liquid biopsies in decentralized clinical studies increasing patient diversity and access, and 4) Design next-generation cures leveraging proprietary nucleic acid chemistries and patients viable cell assays to accelerate the testing of novel drugs and enabling of truly individualized RNA Therapeutics.

We are requesting the maximum of $100,000 as that is still a meaningful contribution to our current fundraising efforts and will allow us to provide a solid data point for the RNA code to RNA Therapeutics chemistry, where we also obtained a fantastic collaborator Novo Nordisk. We are also filing additional grants eg BARDA Drive. 

We would embrace the opportunity to be part of the Cure which we believe - beyond the potential funding - also provides access to additional key collaborators and increased visibility that we will need to succeed.

I have enjoyed participating in the Tuesday talk meetings as well as several events at the Cure which brings collaborators to our doorsteps. 

We are not going to cure cancer alone and as stated in the opening sentence not by using only one drug/approach.

We are aware of Deerfield's many relationships with key academic centers and researchers as well as other tenants of the Cure that could potentially use our solution. 

In particular, we envision that the tools provided for other tenants in the Cure incubator could be augmented with the OneRNA® capabilities as well as outside collaborators and we are in need of additional tools for our cell work. 

Finally, New York’s top-tier academic centers and diverse population can serve as a perfect ground zero for the OneRNAai algorithm generation projects.