Chapter 74
Cancer — 1
We’re stuck in the 1960s in our approach to cancer. By 2050, the number of people dying from cancer is projected to nearly double, but we have not yet incorporated over half a century of extraordinary breakthroughs in the understanding and use of certain biologic mechanisms that can stop cancer from killing us now. No less the implementation of comprehensive prevention strategies such as banning tobacco. We can define risk at the individual level, make the diagnosis very early and more accurately, and then treat patients far more safely and effectively. Since nearly half of us will develop a form of cancer during our lifetime, we can’t forge a major health span expansion without getting our arms around it.
For far too long, we’ve waged a “war” on cancer (presidentially declared in 1971), with too many people losing the battle against it. But enough with the war metaphors. This isn’t a brute force military operation. Health span expansion requires smart use of our cancer knowledge base with the five dimensions of lifestyle, omics, AI, cells, and drugs/vaccines. We know so much about cancer now that we can transform the practice of medicine, and much of that knowledge is ready for you to use.
To get our arms around it, let’s look first at where our understanding of cancer’s biology stands. How does a cancer get started? There are three major theories, which are not mutually exclusive. The basic notion of the somatic mutation theory is that a gene (an “oncogene”) acquires mutations that either activate it or cause it to lose its ability to suppress tumors. As an individual ages, more and more cells die due to instability in the genome, exhaustion of stem cells, dysfunction of mitochondria, and impaired disposal of misfolded proteins and senescent cells. Intriguingly, these processes are the same as the hallmarks of cancer. In our genome of three billion letters, several thousand mistakes occur each time a cell divides. As we age, mutations accumulate in our cells at a rate of fifteen to fifty per cell each year. While most of these don’t give rise to cancer, scientists have discovered over five hundred genes that are more likely to cause cancer when mutations arise in their DNA sequences. These are known as driver genes, because when they are mutated, they drive cell growth and can induce clones of cancer cells to kick off the disease.
In contrast to this cell-based origin theory is one that is tissue based. It occurs locally, where there is some carcinogenic exposure, disruption of tissue, and abnormal immune response. The third theory is tersely known as “bad luck” attributed to a random mistake in the process of stem cell replication. Given the frequency and diversity of cancer, it wouldn’t be at all surprising that all these mechanisms result in cancer one way or another.
The field of spatial biology has transformed our understanding for how cancer evolves (fig. 6.1). Through sequencing of hundreds of thousands, if not millions, of single cells of cancer tissue over time (including their DNA, RNA, proteins, metabolites, and methylation pattern), we are learning precisely how a tumor grows over time and in space. Analysis of these massive datasets requires the use of AI tools and new algorithms. This exquisite spatiotemporal characterization, which can be displayed in 3D, maps out the clones and subclones of cancer cells. It also depicts the new proteins that are expressed on their surface, known as neoantigens. We’ve learned so much about the importance of the tumor microenvironment, the noncancerous ecosystem surrounding the tumor that consists of immune cells, blood vessels, and a large network of proteins and other molecules called the extracellular matrix. The spatial biology 3D maps the tumor microenvironment and the metastatic seeding to other organs.