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The Emperor of All Maladies

Book notes for "The Emperor of All Maladies", by Siddhartha Mukherjee

Highlights:

Last annotated on February 3, 2017

New drugs appeared at an astonishing rate: by 1950, more than half the
medicines in common medical use had been unknown merely a decade
earlier. location 461

Longevity, although certainly the most important contributor to the
prevalence of cancer in the early twentieth century, is probably not the
only contributor. Our capacity to detect cancer earlier and earlier, and
to attribute deaths accurately to it, has also dramatically increased in
the last century. The death of a child with leukemia in the 1850s would
have been attributed to an abscess or infection (or, as Bennett would
have it, to a "suppuration of blood"). And surgery, biopsy, and autopsy
techniques have further sharpened our ability to diagnose cancer. The
introduction of mammography to detect breast cancer early in its course
sharply increased its incidence-a seemingly paradoxical result that
makes perfect sense when we realize that the X-rays allow earlier tumors
to be diagnosed. location 924

A model is a lie that helps you see the truth. -Howard Skipper location
2695

Skipper learned that he could halt this effusive cell division by
administering chemotherapy to the leukemia-engrafted mouse. By charting
the life and death of leukemia cells as they responded to drugs in these
mice, Skipper emerged with two pivotal findings341. First, he found that
chemotherapy typically killed a fixed percentage of cells at any given
instance no matter what the total number of cancer cells was. This
percentage was a unique, cardinal number particular to every drug. In
other words, if you started off with 100,000 leukemia cells in a mouse
and administered a drug that killed 99 percent of those cells in a
single round, then every round would kill cells in a fractional manner,
resulting in fewer and fewer cells after every round of chemotherapy:
100,000 . . . 1,000 . . . 10 . . . and so forth, until the number
finally fell to zero after four rounds. Killing leukemia was an
iterative process, like halving a monster's body, then halving the half,
and halving the remnant half. location 2735

Imagine now that in one of those villages, a new, highly specific test
for cancer is introduced-say the level of a protein Preventin in the
blood as a marker for cancer. Suppose Preventin is a perfect detection
test. Preventin "positive" men and women are thus immediately counted
among those who have cancer. Preventin, let us further suppose, is an
exquisitely sensitive test and reveals very early cancer. Soon after its
introduction, the average age of cancer diagnosis in village 1 thus
shifts from seventy years to sixty years, because earlier and earlier
cancer is being caught by this incredible new test. However, since no
therapeutic intervention is available even after the introduction of
Preventin tests, the average age of death remains identical in both
villages. To a naive observer, the scenario might produce a strange
effect. In village 1, where Preventin screening is active, cancer is now
detected at age sixty and patients die at age eighty-i.e., there is a
twenty-year survival. In village 2, without Preventin screening, cancer
is detected at age seventy and patients die at age eighty-i.e., a
ten-year survival. Yet the "increased" survival cannot be real. How can
Preventin, by its mere existence, have increased survival without any
therapeutic intervention? The answer is immediately obvious: the
increase in survival is, of course, an artifact. Survival rates seem to
increase, although what has really increased is the time from diagnosis
to death because of a screening test. A simple way to avoid this bias is
to not measure survival rates, but overall mortality. (In the example
above, mortality remains unchanged, even after the introduction of the
test for earlier diagnosis.) But here, too, there are profound
methodological glitches. "Cancer-related death" is a raw number in a
cancer registry, a statistic that arises from the diagnosis entered by a
physician when pronouncing a patient dead. The problem with comparing
that raw number over long stretches of time is that the American
population (like any) is gradually aging overall, and the rate of
cancer-related mortality naturally increases with it. Old age inevitably
drags cancer with it, like flotsam on a tide. A nation with a larger
fraction of older citizens will seem more cancer-ridden than a nation
with younger citizens, even if actual cancer mortality has not changed.
location 4461

To compare samples over time, some means is needed to normalize two
populations to the same standard-in effect, by statistically "shrinking"
one into another. This brings us to the crux of the innovation in
Bailar's analysis: to achieve this scaling, he used a particularly
effective form of normalization called age-adjustment. To understand
age-adjustment, imagine two very different populations. One population
is markedly skewed toward young men and women. The second population is
skewed toward older men and women. If one measures the "raw" cancer
deaths, the older-skewed population obviously has more cancer deaths.
Now imagine normalizing the second population such that this age skew is
eliminated. The first population is kept as a reference. The second
population is adjusted: the age-skew is eliminated and the death rate
shrunk proportionally as well. Both populations now contain identical
age-adjusted populations of older and younger men, and the death rate,
adjusted accordingly, yields identical cancer-specific death rates.
Bailar performed this exercise repeatedly over dozens of years: he
divided the population for every year into age cohorts-20-29 years,
30-39 years, 40-49, and so forth-then used the population distribution
from 1980 (chosen arbitrarily as a standard) to convert the population
distributions for all other years into the same distribution. Cancer
rates were adjusted accordingly. Once all the distributions were fitted
into the same standard demographic, the populations could be studied and
compared over time. location 4479

In 1870, the per capita consumption in America615 was less than one
cigarette per year. A mere thirty years later, Americans616 were
consuming 3.5 billion cigarettes and 6 billion cigars every year. By
1953, the average annual consumption of cigarettes had reached
thirty-five hundred per person. On average, an adult American smoked ten
cigarettes617 every day, an average Englishman twelve, and a Scotsman
nearly twenty. location 4666

"By the early 1940s, asking about a connection619 between tobacco and
cancer was like asking about an association between sitting and cancer."
If nearly all men smoked, and only some of them developed cancer, then
how might one tease apart the statistical link between one and the
other? location 4684

It remains an astonishing, disturbing fact that in America-a nation
where nearly every new drug is subjected to rigorous scrutiny as a
potential carcinogen, and even the bare hint of a substance's link to
cancer ignites a firestorm of public hysteria and media anxiety-one of
the most potent and common carcinogens known to humans can be freely
bought and sold at every corner store for a few dollars. location 5455

Notably, the average age of diagnosis of women with such preinvasive
lesions was about twenty years lower than the average age of women with
invasive lesions-once again corroborating the long march of
carcinogenesis. The Pap smear had, in effect, pushed the clock of cancer
detection forward by nearly two decades, and changed the spectrum of
cervical cancer from predominantly incurable to predominantly curable.
location 5727

By the early 1990s, cancer biologists could begin to model the genesis
of cancer in terms of molecular changes in genes. To understand that
model, let us begin with a normal cell, say a lung cell that resides in
the left lung of a forty-year-old fire-safety-equipment installer. One
morning in 1968, a minute sliver of asbestos from his equipment wafts
through the air and lodges in the vicinity of that cell. His body reacts
to the sliver with an inflammation. The cells around the sliver begin to
divide furiously, like a minuscule wound trying to heal, and a small
clump of cells derived from the original cell arises at the site. In one
cell in that clump an accidental mutation occurs in the ras gene. The
mutation creates an activated version of ras. The cell containing the
mutant gene is driven to grow more swiftly than its neighbors and
creates a clump within the original clump of cells. It is not yet a
cancer cell, but a cell in which uncontrolled cell division has partly
been unleashed-cancer's primordial ancestor. A decade passes. The small
collection of ras-mutant cells continues to proliferate, unnoticed, in
the far periphery of the lung. The man smokes cigarettes, and a
carcinogenic chemical in tar reaches the periphery of the lung and
collides with the clump of ras-mutated cells. A cell in this clump
acquires a second mutation in its genes, activating a second oncogene.
Another decade passes. Yet another cell in that secondary mass of cells
is caught in the path of an errant X-ray and acquires yet another
mutation, this time inactivating a tumor suppressor gene. This mutation
has little effect since the cell possesses a second copy of that gene.
But in the next year, another mutation inactivates the second copy of
the tumor suppressor gene, creating a cell that possesses two activated
oncogenes and an inactive tumor suppressor gene. Now a fatal march is
on; an unraveling begins. The cells, now with four mutations, begin to
outgrow their brethren. As the cells grow, they acquire additional
mutations and they activate pathways, resulting in cells even further
adapted for growth and survival. One mutation in the tumor allows it to
incite blood vessels to grow; another mutation within this
blood-nourished tumor allows the tumor to survive even in areas of the
body with low oxygen. Mutant cells beget cells beget cells. A gene that
increases the mobility of the cells is activated in a cell. This cell,
having acquired motility, can migrate through the lung tissue and enter
the bloodstream. A descendant of this mobile cancer cell acquires the
capacity to survive in the bone. This cell, having migrated through the
blood, reaches the outer edge of the pelvis, where it begins yet another
cycle of survival, selection, and colonization. It represents the first
metastasis of a tumor that originated in the lung. The man is
occasionally short of breath. He feels a tingle of pain in the periphery
of his lung. Occasionally, he senses something moving under his rib cage
when he walks. Another year passes, and the sensations accelerate. The
man visits a physician and a CT scan is performed, revealing a rindlike
mass wrapped around a bronchus in the lung. A biopsy reveals lung
cancer. A surgeon examines the man and the CT scan of the chest and
deems the cancer inoperable. Three weeks after that visit, the man
returns to the medical clinic complaining of pain in his ribs and his
hips. A bone scan reveals metastasis to the pelvis and the ribs.
Intravenous chemotherapy is initiated. The cells in the lung tumor
respond. The man soldiers through a punishing regimen of multiple
cell-killing drugs. But during the treatment, one cell in the tumor
acquires yet another mutation that makes it resistant to the drug used
to treat the cancer. Seven months after his initial diagnosis, the tumor
relapses all over the body-in the lungs, the bones, the liver. On the
morning of October, 17, 2004, deeply narcotized on opiates in a hospital
bed in Boston and surrounded by his wife and his children, the man dies
of metastatic lung cancer location 7597

In 2005, an avalanche of papers951 cascading through the scientific
literature converged on a remarkably consistent message-the national
physiognomy of cancer had subtly but fundamentally changed. The
mortality for nearly every major952 form of cancer-lung, breast, colon,
and prostate-had continuously dropped for fifteen straight years. There
had been no single, drastic turn but rather a steady and powerful
attrition: mortality had declined by about 1 percent953 every year. The
rate might sound modest, but its cumulative effect was remarkable:
between 1990 and 2005, the cancer-specific954 death rate had dropped
nearly 15 percent, a decline unprecedented in the history of the
disease. location 7801

The study, reported in 2006, appeared initially to confirm an increased
risk of right-sided brain cancers in men and women who held their phone
on their right ear. But when researchers evaluated the data
meticulously, a puzzling pattern emerged: right-sided cell phone use
reduced the risk of left-sided brain cancer. The simplest logical
explanation for this phenomenon was "recall bias": patients diagnosed
with tumors unconsciously exaggerated the use of cell phones on the same
side of their head, and selectively forgot the use on the other side.
When the authors corrected for this bias, there was no detectable
association between gliomas and cell phone use overall. location 8676

as the fraction of those affected by cancer creeps1059 inexorably in
some nations from one in four to one in three to one in two, cancer
will, indeed, be the new normal-an inevitability. The question then will
not be if we will encounter this immortal illness in our lives, but
when. Atossa location 8923