SPECIAL NOTE REGARDING FORWARD-LOOKING STATEMENTS
This Annual Report on Form 10-K (“Annual Report”) contains forward-looking statements. All statements other than statements of historical facts contained in this Annual Report, including statements regarding our future results of operations and financial position, business strategy, commercial activities and costs, research and development costs, timing and likelihood of success, as well as plans and objectives of management for future operations, are forward-looking statements. These statements involve known and unknown risks, uncertainties and other important factors that are in some cases beyond our control and may cause our actual results, performance or achievements to be materially different from any future results, performance or achievements expressed or implied by the forward-looking statements.
In some cases, you can identify forward-looking statements by terms such as “may,” “will,” “should,” “would,” “expect,” “plan,” “anticipate,” “could,” “intend,” “target,” “project,” “believe,” “estimate,” “predict,” “potential,” or “continue” or the negative of these terms or other similar expressions. Forward-looking statements contained in this Annual Report include, but are not limited to, statements about:
•estimates of our addressable market, market growth, key performance indicators, capital requirements and our needs for additional financing;
•our expectations regarding our financial performance, including among others, revenue, cost of revenue, gross profit, operating expenses, loss from operations and net losses;
•our ability to successfully implement our three phase commercialization plan, including our ability to attract customers during the broad release phase;
•the implementation of our business model, strategic plans and expected pricing for the Proteograph™ Product Suite;
•our expectations regarding the rate and degree of market acceptance of the Proteograph Product Suite;
•the impact of the Proteograph Product Suite on the field of proteomics and the size and growth of the addressable proteomics market;
•competitive companies and technologies and our industry;
•our ability to manage and grow our business;
•our ability to develop and commercialize new products;
•our ability to establish and maintain intellectual property protection for our products or avoid or defend claims of infringement;
•the performance of third-party manufacturers and suppliers;
•the potential effects of government regulation;
•our ability to hire and retain key personnel and to manage our future growth effectively;
•the volatility of the trading price of our Class A common stock;
•the benefits of the PrognomIQ, Inc. transaction;
•the impact of local, regional, and national and international economic conditions and events;
•the impact of COVID-19 on our business; and
•our expectations about market trends.
We have based these forward-looking statements largely on our current expectations and projections about our business, the industry in which we operate and financial trends that we believe may affect our business, financial condition, results of operations and prospects, and these forward-looking statements are not guarantees of future performance or development. These forward-looking statements speak only as of the date of this Annual Report and are subject to a number of risks, uncertainties and assumptions described in the section titled “Risk Factors” and elsewhere in this Annual Report. Because forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified, you should not rely on these forward-looking statements as predictions of future events. The events and circumstances reflected in our forward-looking statements may not be achieved or occur and actual results could differ materially from those projected in the forward-looking statements. Except as required by applicable law, we undertake no obligation to update or revise any forward-looking statements contained herein to reflect events or circumstances after the date of this Annual Report, whether as a result of any new information, future events or otherwise.
In addition, statements that “we believe” and similar statements reflect our beliefs and opinions on the relevant subject. These statements are based upon information available to us as of the date of this Annual Report, and while we believe such information forms a reasonable basis for such statements, such information may be limited or incomplete, and our statements should not be read to indicate that we have conducted an exhaustive inquiry into, or review of, all potentially available relevant information. These statements are inherently uncertain, and you are cautioned not to unduly rely upon these statements.
PART I.
Overview
Our mission is to imagine and pioneer new ways to decode the secrets of the proteome to improve human health. Our initial product, the Proteograph Product Suite (Proteograph), leverages our proprietary engineered nanoparticle (NP) technology to provide unbiased, deep, rapid and large-scale access to the proteome. The Proteograph Product Suite is an integrated solution that is comprised of consumables, an automation instrument and software.
We believe that characterizing and understanding the full complexity of the proteome is foundational for accelerating biological insights and will lead to broad potential end-markets for proteomics, encompassing basic research and discovery, translational research, diagnostics and applied applications. This full understanding of the complexity of the proteome and its dynamic nature requires large-scale, unbiased and deep interrogation of thousands of samples across time, which we believe is unavailable with the proteomic approaches available today. We believe that the Proteograph Product Suite has the potential to enable researchers to perform these proteomics studies at scale.
Proteins are the functional units of many biological processes and dynamic indicators of physiology that can gauge health over time, inform disease progression and monitor therapeutic response. Despite the central role proteins play in biology, rich functional content derived from proteomics studies is relatively unexplored compared to the genome, since large-scale proteomic studies have not been possible. We believe large-scale characterization of the proteome has not been feasible with existing proteomics approaches, which broadly fall into two categories: (i) unbiased but not scalable, or (ii) scalable but biased. Current de novo, or unbiased, approaches require complex, lengthy, and labor- and capital-intensive workflows that limit their scalability to small, under-powered studies. Targeted or biased methods enable interrogation of a limited number of known proteins per sample. Although targeted approaches are scalable, they lack the breadth and depth necessary to appropriately characterize the proteome and catalog its many protein variants. Therefore, we believe that proteomics researchers are forced into an unattractive trade-off between the number of samples in a study and the depth and breadth of the analysis. These trade-offs limit the ability to advance characterization of the proteome to match the characterization of the genome. We believe deep, unbiased, large-scale proteomic analysis is needed for a more complete understanding of biology.
We are initially focused on driving adoption of the Proteograph with customers in the proteomics and genomics markets, with those researchers who recognize the value of large-scale, unbiased, deep proteomics. Allied Market Research estimates the proteomics market was $32 billion in 2019. We believe that the Proteograph’s unique capabilities will enable researchers to undertake unbiased studies not possible today, particularly those of larger scale, and will complement genomics studies by adding critical missing information that can provide functional context to genomic variation. According to the dbSNP database, over 1 billion individual genetic variants have been identified to date; however, fewer than 0.2% of those variants have been cataloged in the ClinVar database with a reported relationship between variation and phenotype.
We believe unbiased, deep and large-scale proteomics will help researchers map biological function of genomic variants, identify impactful disease and response-specific risk factors, and accelerate discovery of molecular mechanisms of health and disease. We believe these capabilities should broadly appeal to researchers and entities undertaking large-scale genomics studies and should attract spending from the genomics market, estimated by Technavio to be $21 billion in 2019. In addition, we believe the Proteograph is likely to enable novel content discovery that will lead to entirely new applications and market opportunities.
We are initially focused on research applications for the Proteograph Product Suite and are selling and marketing the Proteograph for research use only (RUO). We commenced the third and final phase of our commercialization plan with broad release in January 2022.
The Importance of Proteomics
Detailed and complex biological information resides at the protein level. Virtually every function within a living organism occurs by the action of a protein or a group of proteins interacting with each other and in concert. Thus, proteomics is a key area of focus for researchers. Proteins are dynamic indicators of health status and can be used to monitor disease progression and therapeutic response. By contrast, the genome is a static indicator of what a person’s physiology could be, not an indicator of current physiological state. In short, the genome represents risk, while the proteome reflects status. Despite the physiological impact, the human proteome is relatively unexplored compared to the human genome.
Through large-scale data collection and widespread adoption of molecular profiling techniques, over one billion genetic variations have been identified across all genomes that have been sequenced. Although this information has significantly improved the understanding of biology, the functional context at the protein level has not been established for the vast majority of this genomics information. In other words, researchers have not been able to connect phenotypic information with the relevant genotypic information. We believe that if we enable researchers to generate large bodies of proteomic data that can be coupled with large bodies of genomic data, they will be better positioned to understand the relationship between variation and function and its impact on biology.
Challenges of Accessing the Proteome
The human proteome is more dynamic, diverse and complex in structure, composition and number of variants than either the genome or transcriptome. Starting from the genome, multiple biological steps take place to arrive at the proteome, each step creates increased complexity and diversity. The human genome has approximately 20,000 genes, which are estimated to give rise to more than 200,000 transcripts, which then give rise to 1,000,000 or more protein variants. As shown in Figure 2, this is in part because a single gene produces distinct ribonucleic acid (RNA) isoforms through the process of transcription and a myriad of structurally distinct proteins through the process of translation. Biological processes can further chemically modify these proteins in unique ways, resulting in a large number of protein variants through post-translational modifications. Overall, these processes result in many levels of protein diversity, from amino acid sequence and structural variations, to post-translational modifications (PTMs), to functional changes due to interactions between the proteins themselves, known as protein-protein interactions
(PPIs). We believe the fundamental challenge with existing proteomics methods is their inability to measure the breadth and depth of the proteome’s complexity, rapidly and at scale.