Cancer exacts a steep social and economic toll, especially as an aging world population creates increased cancer rates. The investment community plays a critical role in supporting the conversion of entrepreneurial innovation into products and platforms that reduce the burden of cancer throughout the world. A viable investment strategy rests on an understanding of the scientific and technological advances that are rapidly being made in the cancer field, and where those advances are heading. These breakthroughs are impacting all aspects of cancer treatment, from drug development to integrated pathways of care delivery.
So, how will today’s unprecedented biological insights and technological prowess, shape investment opportunities in the near future and coming decade? What follows are three research and development areas that I believe offer significant opportunities. All three laid the foundation for the current golden era of cancer progress.
Precision cancer therapy had its origins in the recombinant DNA technology revolution of the 1970s, the human genome project of the 1990s and the human cancer genome atlas project of the 2000s. These programs provided a comprehensive atlas of the myriad genetic aberrations present across virtually all cancer types and helped identify critical genetic vulnerabilities in cancer and guide the development of ‘targeted therapies’ directed against specific genetic mutations. Now profiling a patient’s tumor can identify those patients most likely to benefit from specific treatments.
In 2020, there were two breakthroughs in early cancer detection. One is an assay capable of detecting 50+ different cancer types in a single blood sample, with a very low (< 1%) false positivity rate. The other is the ability to perceive cancer several years earlier than standard diagnoses. In addition to development of the tests themselves, digital health platforms to guide diagnostic workup and follow-up will represent potential huge opportunities for new company investments.
Cancer immune therapy impacts the care of nearly all cancer patients today. Decades of fundamental research has elucidated the mechanisms governing immune system regulation and how it is suppressed in cancer. We now understand and can deactivate specific immune suppression mechanisms in the tumor enabling immune cells to identify and attack cancer cells.
My biggest bets for the future are on drugs targeting multiple checkpoint blockade molecules via bi-specific antibodies, activating immune recognition and neutralizing myeloid suppressor cells. Tumor antigens must be processed and presented to T cells in a specific format. Strategies that can provide off-the-shelf tumor antigens and/or activate antigen-presenting cells to process naturally presented tumor antigens have the potential to be game changers.
Exciting agents are being developed in the area of myeloid suppressor cells, which often constitute the bulk of the tumor mass and can profoundly dampen killer T cell responses. When combined with immune checkpoint inhibitor therapy, some of these agents have shown cure of human cancer – in mouse models – and are now entering early stage clinical trials. I believe that competitive advantages will come from the data informing these drug discoveries and clinical developments. Having high-resolution real-world patient-level data (not a cell line or a piece of tumor) will be key to dissecting and delineating anti-self, versus anti-tumor activity of an immune system target, which will greatly inform the development path of potential drugs.
Artificial intelligence (AI) and machine learning (ML) is transforming virtually all aspects of cancer research, drug development, clinical trials and patient care. Its emerging dominance is propelled by the explosion of big data and maturation of the cloud. As our ability to generate more health-related data grows, we need the computational power of AI/ML to help make sense and make use it. Such innovations also require the big data capacity of the Cloud. Cloud service platforms make previously inaccessible IT resource and capabilities available and affordable to the masses, removing the barriers to entry for small research groups and startup entrepreneurs, and creating a rich playing field. Indeed, the last decade witnessed disruptive innovations across various areas of discovery, development and care.
The key to success for AI/ML innovations is the human and molecular data fueling the AI engines. Not only do these innovations need access to primary data (e.g. pathology slides, radiology images), they need accurate clinical annotation to train a novel algorithm, including multiple sources from diverse patient populations. Companies with innovative strategies to obtain these data will have a huge competitive advantage in this space.
The application of AI/ML in clinical practice is perhaps the greatest opportunity for both improved patient care and for investment. Here, one of the most exciting areas is developing connector products that bridge the complex and diverse healthcare teams comprised of service centers, healthcare providers, and mobile apps. These services, and the people involved in delivering them, have to be well-coordinated for patients to benefit. Clinical intelligence systems powered by real-time data and AI/ML will be key to connecting these solutions and providers to deliver a new generation of cancer care to patients.
Lastly, it is necessary to reiterate the elephant in the room with regard to applications of cutting-edge technologies like AI/ML in cancer. It is not about the shiniest object. Too many times we have seen the doom of companies that developed technology solutions to search for problems. I would be more excited about companies that understand the problems first. One example is Apricity Health, an innovation company founded by renowned cancer experts, immunologists and genomic scientists, whose motto is to “lead with science and clinical expertise.” Their strategy and focus is to assemble world-class experts to identify bottlenecks and imagine solutions, then invest in technology development to create products and services, with the ultimate goal of “bring cancer centers closer to patients – “in the community, at home and on the cloud” – so access to expert cancer care is equitable.
We are living in a golden era of cancer research. In the coming decade, prevention and detection technology will catch cancers at earlier, more curable stages; targeted and immune treatments will lead to greater percentages of cure for advanced disease; and AI-driven digital platforms will provide access to first-rate affordable care, particularly to the underserved.
References:
- U.S. Food & Drug Administration. COVID-19 vaccines. https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/covid-19-vaccines
- Centers for Disease Control and Prevention. Facts about COVID-19 Vaccines. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/facts.html
- Michael D. Crowther MD, et al. Genome-wide CRISPR–Cas9 screening reveals ubiquitous T cell cancer targeting via the monomorphic MHC class I-related protein MR1. Nature Immunology. 2020; 21:178–185.
- Neelapu SS, et al. “Interim analysis of ZUMA-12: A phase 2 study of axicabtagene ciloleucel (axi-cel) as first-line therapy in patients with high-risk large B cell lymphoma.” ASH 2020; Abstract 405.
- Callaway E. “It Will Change Everything”: AI makes gigantic leap in solving protein structures. Nature. 2020; December 10; Vol 588.
- Kuenzi BM, et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell. 2020; 38(5): 672-684.e6.
- Sasaki K, et al. The LEukemia Artificial Intelligence Program (LEAP) in chronic myeloid leukemia in chronic phase: A model to improve patient outcomes. Am J Hematol. 2020; doi: 10.1002/ajh.26047. Online ahead of print.
- Bhattacharya T, et al., AI Meets Exascale Computing: Advancing cancer research with large-scale high performance computing. Front Oncol. 2019; 9:984. doi: 10.3389/fonc.2019.00984. eCollection 2019.
- Lui MC, et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Annals of Oncology. 2020; 31: 745-759.
- Chen, X., et al. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test. Nat Commun. 2020; 11: 3475.
- Callaway E. Revolutionary microscopy technique sees individual atoms. Nature. 2020; June 11; Vol 582.
- David S. Hong, et al. KRASG12C inhibition with sotorasib in advanced solid tumors. NEJM. 2020; 383: 1207-1217.
- Dimopoulos MA, et al. Weekly selinexor, bortezomib, and dexamethasone (SVd) versus twice weekly bortezomib and dexamethasone (Vd) in patients with multiple myeloma (MM) after one to three prior therapies: Initial results of the phase III BOSTON study. Journal of Clinical Oncology 38(15):suppl (May 20, 2020) 8501-8501.