Prática de Shadowing: Why Can’t We Cure Cancer Yet? - Aprenda a falar inglês com vídeo
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We declared war on cancer in 1971.
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Not metaphorically, literally.
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The United States National Cancer Act was signed into law by
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Richard Nixon with the explicit intention of doing to cancer what the space program had done to the moon landing problem.
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Throw enough money, enough scientists, and enough national will at it until it surrendered.
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The logic seemed sound.
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We had just put humans on the moon.
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Surely a disease was easier than that.
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It has now been more than 50 years.
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Cancer kills approximately 10 million people per year globally.
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It is the second leading cause of death worldwide.
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And while the treatments have improved,
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meaningfully, genuinely improved, the word cure still doesn't appear in most conversations about most cancers without significant qualification attached to it.
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So what happened?
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Was the science wrong?
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Were the researchers incompetent?
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Did we simply not spend enough money?
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Because the global cancer treatment industry is now worth hundreds of billions of dollars annually so it isn't obviously a resource problem?
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Or is cancer something fundamentally different from the kind of problem that throwing resources at actually solves?
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The answer is the last one.
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And understanding why requires understanding what cancer actually is, which turns out to be one of the most important things most people have never been told clearly.
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Here is the thing that changes everything when you really absorb it.
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Cancer is your own cells.
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Your own DNA.
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A. Your own biology, running a program that your body itself wrote, using machinery that your body built, following rules that evolution installed.
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It is not a pathogen, not a bacterium, not a virus that has entered your body from outside and is attacking your cells from a foreign position.
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This is the fundamental reason why curing cancer is categorically harder than curing an infection.
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When a bacterium enters your body, it is a foreign object with foreign proteins that your immune system can learn to recognize and attack.
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The boundary between self and not-self is clear.
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The target is identifiable.
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The strategy, kill the foreign thing without killing the host, is at least conceptually straightforward.
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Cancer collapses that boundary.
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The target is you.
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Specifically, it is a version of you that has stopped following the rules that keep the rest of you functional.
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Killing cancer without killing the host is one of the hardest targeting problems in the history of medicine, because the thing you're trying to destroy is made of the same materials,
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running on the same biochemistry, and protected by the same structures as everything you're trying to preserve.
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This is why chemotherapy, the dominant cancer treatment for decades, works the way it does, and fails the way it does.
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Chemotherapy drugs target rapidly dividing cells, which cancer cells are.
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But rapidly dividing cells also include hair follicles, gut lining, bone marrow, and immune system cells.
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The drug doesn't know the difference.
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It targets the behavior, rapid division, and the identity problem is what makes cancer so persistently hard to solve.
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Let's go deeper into the biology because this is where the real complexity lives and where most popular explanations stop short.
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Your body contains roughly 37 trillion cells.
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Every one of them carries a complete copy of your genome, about 3 billion base pairs of DNA.
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Every time a cell divides, it has to copy all three billion of those base pairs.
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And copying is imperfect.
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Every cell division introduces somewhere between one and ten random mutations.
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Your body performs trillions of cell divisions over your lifetime.
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The math is not reassuring, but your body has defenses, multiple layers of them, tumor suppressor genes that apply breaks to the cell division process,
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proofreading mechanisms that catch and correct copying errors,
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apoptosis, programmed cell death, that kills cells that are too damaged to repair safely,
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and the immune system, which patrols continuously for cells behaving abnormally and eliminates them before they can proliferate.
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Cancer happens when enough of these defenses fail simultaneously.
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A typical cancer cell carries hundreds of mutations, accumulated over years or decades, each one slightly loosening the constraints on cell behavior,
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until a cell emerges that grows without limit, resists death signals, recruits blood vessels to feed itself, and eventually spreads to other tissues.
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This is cancer as an evolutionary process.
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And this framing, cancer as evolution happening inside your body, is the most important conceptual shift in cancer biology of the last 20 years.
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Cancer cells compete with each other.
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The mutations that help one cell grow faster than its neighbors get selected for, the same way beneficial mutations get selected for in populations of organisms.
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The tumor is an ecosystem of competing cell populations,
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each slightly different, each under selection pressure to outgrow the others and resist whatever the treatment is trying to do to them.
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This is why cancer develops resistance to treatment.
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When you hit a tumor with a drug, you kill the cells most vulnerable to that drug.
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But some cells, by random mutation, happen to be slightly less vulnerable.
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Those cells survive.
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They reproduce.
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The tumor that grows back is descended from the survivors, the ones that already had some resistance.
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You have, through treatment, accidentally run a selection process for drug resistant cancer.
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Evolution at work, inside a patient, during therapy.
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This consequence of the underlying biology cannot be fully escaped without solving the evolutionary dynamics of the tumor.
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Which is a much harder problem than simply finding a drug that kills cancer cells.
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Now let's talk about why the phrase curing cancer is itself part of the problem.
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Cancer is over 200 distinct diseases unified by the characteristic of uncontrolled cell growth but differing in their cell type of origin,
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their genetic mutations, their growth patterns, their metastatic behavior, their response to treatment, and their prognosis.
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Lung cancer and leukemia and breast cancer and glioblastoma and melanoma are different problems that share a broad category.
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Treating them as variations on a single disease is like treating pneumonia
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and tuberculosis and asthma as the same problem because they all affect the lungs.
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Even within a single named cancer type, the variation is enormous.
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Two patients with stage 3 lung cancer may have tumors with completely different genetic profiles,
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different immune microenvironments, different metabolic characteristics, and different responses to the same treatment.
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What works for one may fail completely for the other, because the specific cancer being treated is biologically different at the molecular level.
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This is why clinical trials are so difficult, and why results that look promising in the lab so frequently fail to translate to patients.
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The patient population is not uniform.
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The disease is not uniform.
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The drug
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that works beautifully on a cell line in a dish may work on 30% of patients with a particular cancer type
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and do nothing for the other 70%.
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And the difference between those groups may not be obvious until after the trial.
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Personalized medicine, tailoring treatment to the specific genetic profile of each patient's specific tumor, is the direction the field is moving,
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and it has produced real results in specific cancer types.
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But sequencing a tumor's genome, and identifying the actionable mutations, and designing a treatment that targets those specific mutations in that specific patient is expensive,
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slow, and complicated by the fact that the tumor evolves during treatment, changing its genetic profile as it responds to selective pressure.
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The target moves in real time.
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Let's talk about the immune system, because this is where the most exciting recent progress has happened and where the complexity of the problem is most visible.
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The immune system should be killing cancer.
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It should be the body's primary defense against tumor development, detecting abnormal cells and eliminating them before they become established tumors.
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And in fact, it does this continuously.
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Most cancer cells are killed by the immune system before they ever become a tumor, you'd notice.
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The cancers that become clinical problems are, by definition, the ones that have evaded immune detection.
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Cancer cells evade it through a variety of mechanisms, many of which involve actively suppressing immune function in their local environment.
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They express surface proteins that signal to immune cells not to attack.
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They recruit immune cells into the tumor and reprogram them to suppress anti-tumor activity.
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They create a local immunosuppressive environment that disables the very cells that should be destroying them.
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Immunotherapy, the approach of reactivating the E, immune systems attack on cancer has produced some of the most dramatic results in cancer treatment in recent history.
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Checkpoint inhibitors, which remove the molecular breaks that cancer cells apply to immune function have produced long-term remissions in patients with melanoma,
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lung cancer, and other previously lethal cancers who had no other options.
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Patients who would have died in months are alive years later, but checkpoint inhibitors work in approximately 20 to 40 percent of patients with responsive cancer types
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and often don't work at all in others.
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The tumors that don't respond have found other ways to suppress immune function that the checkpoint inhibitors don't address.
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Some patients develop severe autoimmune side effects when the immune breaks are removed, because those breaks exist for a reason,
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which is to prevent the immune system from attacking normal tissue.
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The immune system is an extraordinarily complex system with multiple competing functions, evolved to balance aggression against foreign threats with tolerance of self-tissue.
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Cancer exploits the tolerance mechanisms to hide.
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that tolerance to attack cancer also risks attacking everything else.
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The trade-off has no clean solution.
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See you inside.
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Now let's talk about metastasis, because this is where cancer becomes lethal and where treatment becomes most difficult.
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The primary tumor, the original site of cancer, is rarely what kills.
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Surgery can remove it.
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Radiation can target it.
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The problem is when cancer cells break away from the primary tumor, enter the bloodstream or lymphatic system,
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travel to distant organs, and establish secondary tumors.
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By the time metastasis is detected, it has usually already happened in multiple sites.
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A patient whose cancer has spread to the liver, lungs, and bone simultaneously has three separate tumors to treat, each potentially with a different genetic profile,
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each in a different tissue environment, each potentially developing resistance independently.
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Cancer cells completing metastasis have to accomplish an extraordinary series of steps.
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They detach from the primary tumor, survive in the hostile environment of the bloodstream, evade immune attack while circulating,
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arrive at a distant organ, penetrate its tissue, survive in an entirely different microenvironment, and then reactivate growth.
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Each step is a potential point of intervention.
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None of them is easy to block without disrupting normal biological processes that use similar mechanisms.
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And then there is dormancy.
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Cancer cells can enter a metabolically quiet state, not dividing, invisible to treatments that target dividing cells and nearly invisible to imaging,
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and remain dormant in distant tissues for years or decades before reactivating.
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A patient declared cancer-free after treatment may harbor dormant cancer cells that reactivate years later under conditions not fully understood.
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This is why oncologists use five-year survival rates, rather than the word cured.
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The dormancy problem means that being cancer-free today does not guarantee being cancer-free in 10 years.
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Let's talk about the gap between laboratory results and patient outcomes, because this is one of the most frustrating recurring patterns in cancer research,
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and one that the public rarely sees clearly.
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A drug works in a mouse.
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Researchers publish the results.
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Headlines Americans announce a potential breakthrough.
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The drug enters human trials.
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It fails.
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This cycle has repeated hundreds of times across cancer research history, and its repetition is not evidence of incompetence or fraud.
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It is evidence of a genuine biological gap between the experimental models used to test drugs and the actual human disease.
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Mouse tumors are genetically uniform, implanted in immunocompromised animals in controlled laboratory conditions.
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Human tumors are genetically heterogeneous, arising in immunologically complex individuals with distinct genetic backgrounds,
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varied exposures, and decades of accumulated biological history.
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A drug
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that clears a tumor in a mouse is operating on a
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fundamentally simpler biological system than the one it will encounter in a human patient.
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The simplification that makes laboratory research tractable also makes it systematically misleading about what will happen in a real clinical setting.
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This problem is known.
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Researchers work to improve their models, better mouse models with more intact immune systems, organoids grown from patient tumor tissue, computational models of tumor evolution.
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Progress is real.
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The gap has not closed completely, and may never close completely, because the full complexity of a human patient cannot be replicated in any experimental system.
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Let's talk about early detection, because this is the dimension of cancer control where where the potential for impact is highest
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and where the science is advancing most rapidly in ways that don't always make headlines.
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The single most powerful predictor of cancer survival is how early the disease is detected.
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Stage 1 breast cancer has a 5-year survival rate above 90%.
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Stage 4 breast cancer has a 5-year survival rate below 30%.
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The biology of the cancer may be the same.
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The outcome is determined largely by how much time the cancer had to grow and spread before it was found.
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Liquid biopsy, the detection of cancer DNA shed by tumors into the bloodstream, represents one of the most promising current approaches to early detection.
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A blood test that could detect cancer before symptoms appear, across multiple cancer types from a routine blood draw.
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Several such tests are in clinical development and early deployment.
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The challenge is sensitivity and specificity.
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Detecting enough cancer signal to find early-stage tumors
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while generating few enough false positives to be useful in screening populations where most people don't have cancer,
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multi-cancer early detection tests are being evaluated in large clinical trials right now.
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The results will take years to fully emerge because demonstrating
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that a test improves outcomes requires following patients long enough to see whether early detection actually translates to lives saved.
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But the direction is promising in ways that treatment alone has never been.
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Finding cancer earlier does not cure it in the biological sense, but it dramatically changes the odds of the treatments we already have working well enough to matter.
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Let's talk about money, because the economics of cancer treatment create incentive structures
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that affect what gets researched and what gets developed in ways that are important and underappreciated.
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Drug development is extraordinarily expensive.
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A new cancer drug costs an estimated $2-3 billion to bring from early research through through clinical trials to approval.
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This investment requires a return, which means pharmaceutical companies focus development efforts on drugs that can be sold at prices that justify the investment.
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This creates a structural bias toward treatment rather than prevention.
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A treatment can be sold repeatedly to a large market of patients.
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Prevention is harder to monetize because the patient who doesn't get cancer doesn't know they were supposed to.
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It also creates pressure toward drugs that extend survival by measurable periods, months or years, even when they don't cure.
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A treatment that gives a patient six additional months of life is demonstrable and reimbursable.
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A treatment that theoretically prevents recurrence in a patient already declared cancer-free is much harder to sell,
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because demonstrating prevention requires following patients for decades to show the recurrence didn't happen.
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Publicly funded research fills some of this gap,
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but public research funding operates on grant cycles and publication incentives that create their own structural pressures toward incremental,
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publishable results rather than high-risk work that might not produce results within the grant period.
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The system around the science shapes what the science investigates and how fast it moves, independently of what the biology actually requires.
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Now let's talk about what is actually working, because the picture is not uniformly bleak, and the progress deserves precise acknowledgement.
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Childhood leukemia, once nearly universally fatal, is now curable in approximately 90% of cases.
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Hodgkin lymphoma has been transformed from a death sentence to a highly treatable disease.
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Certain types of breast cancer caught early have 5-year survival rates above 90%.
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CAR T-cell therapy, a technique that engineers a patient's own immune cells to recognize and attack cancer,
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has produced remarkable results in certain blood cancers, with some patients achieving complete remissions after every other treatment had failed.
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The pattern in these successes is informative.
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The cancers most successfully treated tend to be genetically simpler, more accessible, to the immune system or to circulating drugs,
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and have identifiable genetic drivers that can be specifically targeted.
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The cancers that remain most resistant tend to be genetically complex,
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immunologically evasive, and anatomically difficult to access.
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The progress is real.
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The remaining problems are genuinely, deeply, biologically hard.
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Both are true.
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Let's talk about where the field is actually heading, because the next decade of cancer research looks different from the previous five decades, and the difference matters.
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Artificial intelligence is changing the speed at which researchers can analyze the enormous datasets that modern cancer biology generates.
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A human researcher cannot simultaneously hold in mind the mutational profiles of 10,000 tumors, the treatment responses of those patients,
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the genetic backgrounds of the individuals involved and the thousands of potential drug interactions relevant to those profiles.
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Computational systems can, at least partially, and are producing genuine insights about cancer subtypes,
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treatment response prediction, and drug target identification that would not have emerged from human analysis alone.
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MRNA technology, which became widely known through COVID vaccines, has potential applications in cancer treatment that researchers have been exploring for years.
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The same platform that can instruct cells to produce a viral protein can potentially instruct immune cells to recognize cancer-specific proteins,
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producing personalized cancer vaccines tailored to the specific mutational profile of each patient's tumor.
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Early trials in melanoma and pancreatic cancer have shown promise.
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The convergence of better biological understanding, better computational tools, better early detection,
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and better immunological approaches is producing a different landscape than the one that existed when Nixon signed the National Cancer Act.
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Progress is not a straight line, and will not be, but the direction is real.
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And this is ultimately the answer to the question in the title.
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We cannot cure cancer yet because cancer is one of the hardest problems that biology poses to medicine.
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A moving target built from the body's own machinery, evolving under treatment, distributed across 200 distinct diseases each with its own biology,
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operating through mechanisms intertwined with the normal cellular processes that keep us alive.
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Every drug that kills cancer cells risks killing normal cells.
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Every immune activation risks autoimmunity.
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Every targeted therapy produces resistance.
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Every success in one cancer type reveals the unique complexity of the next.
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Cancer will not be defeated by a single cure.
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The biology makes that impossible.
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What will happen, what is already happening,
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is that specific cancers will be understood and controlled one by one through the specific biology of each disease.
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Childhood leukemia first, then Hodgkin lymphoma, then certain breast cancers.
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The list will grow.
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It will grow slowly by the standards of news cycles and quickly by the standards of how medicine has historically moved.
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The war Nixon declared was never really one war.
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It was 200 separate wars fought simultaneously against enemies that keep changing their tactics.
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Some of those wars are already won.
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Others are being won now.
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Others will take longer than any of us would like.
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But the people fighting them are not losing.
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They are learning, and in medicine, learning and winning are eventually the same thing.
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Vocabulário e dicas de fala para esta lição
Esta aula de conversação de nível C1 usa o vídeo “Why Can’t We Cure Cancer Yet?”. As palavras que mais se repetem: cancer, cell, tumor, treatment, patient. Este vídeo tem 243 frases e 3321 palavras para praticar shadowing. A fala dura 21:48. O falante fala em ritmo natural, cerca de 152 palavras por minuto, próximo de uma conversa do dia a dia. Apenas 77% das palavras estão entre as 3.000 mais comuns do inglês, por isso o vocabulário é exigente.
Vocabulário principal deste vídeo
As 15 palavras mais avançadas do vídeo, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| tumor substantivo | /tuː.məɹ/ | tumor |
| mutation substantivo | /mjuˈteɪʃən/ | mutação |
| detection substantivo | /dɪˈtɛk.ʃən/ | detecção, deteção |
| researcher substantivo | /ˈɹiˌsɝ.t͡ʃɚ/ | pesquisador, pesquisadora |
| genetically advérbio | /d͡ʒəˈneɾɪkli/ | geneticamente |
| complexity substantivo | /kəmˈplɛk.sɪ.ti/ | complexidade |
| detect verbo | /dɪˈtɛkt/ | detectar, detetar |
| bloodstream substantivo | /ˈblʌd.stɹɪim/ | corrente sanguínea |
| checkpoint substantivo | /ˈt͡ʃɛkˌpɔɪnt/ | ponto de controle, ponto de controlo |
| inhibitor substantivo | /ɪnˈhɪbɪtɚ/ | inibidor |
| leukemia substantivo | /luːˈkiːmi.ə/ | leucemia |
| melanoma substantivo | /mɛləˈnoʊmə/ | melanoma |
| computational adjetivo | /ˌkɑmpjʊˈteɪʃənəl/ | computacional |
| tolerance substantivo | /ˈtɑ.lə.ɹəns/ | tolerância |
| accumulate verbo | /əˈkjuːmjʊˌleɪt/ | acumular |
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | has now been · have improved · have never been told |
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | was signed · been told · is made |
| Orações relativas who / which + oração — informação extra sobre uma pessoa ou coisa | ones that have · return, which means |
Pronúncia para ficar de olho
O falante usa 15 contrações e formas reduzidas, como don't, doesn't, you're. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “th”: chemotherapy /ˌkiːməʊˈθɛɹəpi/, lethal /ˈliː.θəl/
- Os sons de “sh” e “zh”: mutation /mjuˈteɪʃən/, detection /dɪˈtɛk.ʃən/, computational /ˌkɑmpjʊˈteɪʃənəl/, remission /ɹəˈmɪʃ(ə)n/, evolutionary /ˌiː.vəˈluː.ʃən.ɛɹ.i/
- Palavras longas — acerte a sílaba tônica: genetically /d͡ʒəˈneɾɪkli/, complexity /kəmˈplɛk.sɪ.ti/, inhibitor /ɪnˈhɪbɪtɚ/, leukemia /luːˈkiːmi.ə/, melanoma /mɛləˈnoʊmə/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: detect /dɪˈtɛkt/, checkpoint /ˈt͡ʃɛkˌpɔɪnt/, accumulate /əˈkjuːmjʊˌleɪt/, circulate /ˈsɝ.kjʊˌleɪt/, dormant /ˈdɔːmənt/
- /l/ final — a língua toca o céu da boca, não vira “u”: computational /ˌkɑmpjʊˈteɪʃənəl/, lethal /ˈliː.θəl/, invisible /ɪnˈvɪzəb(ə)l/
- /h/ — um sopro suave, diferente do “r”: inhibitor /ɪnˈhɪbɪtɚ/, headline /ˈhɛd.laɪn/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Comece na velocidade 0,75×, faça shadowing frase por frase e volte à velocidade normal quando ficar fácil.
- Grave a sua voz e compare com o original, prestando atenção a palavras como tumor, mutation, detection.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.