10 Newly-Defined Molecular Types of Breast Cancer in Nature, and a Dream

Breast cancer is not one disease. We’ve understood this for decades. Still, and with few exceptions, knowledge of BC genetics – information on tumor-driving DNA mutations within the malignant cells – has been lacking. Most patients today get essentially primitive treatments like surgical hacking, or carving, traditional chemotherapy and radiation. Some doctors consider hormone therapy as targeted, and thereby modern and less toxic. I don’t.

Until there’s a way to prevent BC, we need better ways to treat it. Which is why, upon reading the new paper in Nature on genetic patterns in breast cancer, I stayed up late, genuinely excited. As in thrilled, optimistic..The research defined 10 molecular BC subgroups. The distinct mutations and gene expression patterns confirm and suggest new targets for future, better therapy.

The work is an exquisite application of science in medicine. Nature lists 31 individuals and one multinational research group, METABRIC (Molecular Taxonomy of Breast Cancer International Consortium), as authors. The two correspondents, Drs. Carlos Caldas and Samuel Aparicio, are based at the University of Cambridge, in England, and the University of British Columbia in Vancouver, Canada. Given the vastness of the supporting data, such a roster seems appropriate, needed. The paper, strangely and for all its worth, didn’t get much press –

Just to keep this in perspective – we’re talking about human breast cancer. No mice.

The researchers examined nearly 2000 BC specimens for genetic aberrations, in 2 parts. First, they looked at inherited and acquired mutations in DNA extracted from tumors and, when available, from nearby, normal cells, in 997 cancer specimens – the “discovery set.” They checked to see how the genetic changes (SNPs, CNAs and/or CNVs) correlated with gene expression “landscapes” by probing for nearly 29,000 RNAs. They found that both inherited and acquired mutations can influence BC gene expression. Some effects of “driver” mutations take place on distant chromosomal elements, in what’s called a trans effect; others happen nearby (cis).

Next, they honed in on 45 regions of DNA associated with outlying gene expression. This led the investigators to discover putative cancer-causing mutations (accessible in supplementary Tables 22-24, available here). The list includes genes that someone like me, who’s been out of the research field for 10 years, might recall – PTEN, MYC, CDK3 and -4, and others. They discovered that 3 genes, PPP2R2A, MTAP and MAP2K4 are deleted in some BC cases and may be causative. In particular, they suggest that loss of PPP2R2A may contribute to luminal B breast cancer pathology. They find deletion of MAP2K4 in ER positive tumors, indicative of a possible tumor suppressor function for this gene in BC.

Curtis, et al. in "Nature": April 2012

The investigators looked for genetic “hotspots.” They show these in Manhattan plots, among other cool graphs and hard figures, on abnormal gene copy numbers (CNAs) linked to big changes in gene expression. Of interest to tumor immunologists (and everyone else, surely!), they located two regions in the T-cell receptor genes that might relate to immune responses in BC. They delineated a part of chromosome 5, where deletions in basal-like tumors marked for changes in cell cycle, DNA repair and cell death-related genes. And more –

Cluster Analysis (abstracted), Wikipedia

Heading toward the clinic, almost there…

They performed integrative cluster analyses and defined 10 distinct molecular BC subtypes. The new categories of the disease, memorably labeled “IntClust 1-10,” cross older pathology classifications (open-access: Supplementary Figure 31) and, it turns out, offer prognostic information based on long-term Kaplan-Meier analyses (Figure 5A in the paper: Supplementary Fig 34 and 35). Of note, here, and a bit scary for readers like me, is identification of an ER-positive group, “IntClust 2” with 11q13/14 mutations. This BC genotype appears to carry a much lesser prognosis than most ER-positive cases.

Finally, in what’s tantamount to a 2nd report, the researchers probed a “validation set” of 995 additional BC specimens. In a partially-shortened method, they checked to see if the same 10 molecular subtypes would emerge upon a clustering analysis of paired DNA mutations with expression profiles. What’s more, the prognostic (survival) information held up in the confirmatory evaluation. Based on the mutations and gene expression patterns in each subgroup, there are implications for therapy. Wow!

I won’t review the features of each type here for several reasons. These are preliminary findings, in the sense that it’s a new report, albeit a model of what’s a non-incremental published set of observations and analysis; it’s early for patients – but not for investigators – to act on these findings. (Hopefully, this will not be the case in 2015, or sooner, preferably, for testing some pertinent drugs in at least a subset of the subgroups identified.) Also, some of the methods these authors used came out in the past decade, after I stopped doing research. It would be hard for most doctors to fully appreciate the nuances, strengths and weaknesses of the study.

Most readers can’t know how skeptical I was in the 1990s, when grant reviewers at the NCI seemed to believe that genetic info would be the cure-all for most and possibly all cancers. I don’t think that’s true, nor due most people involved with the Human Genome Project, anymore. The Cancer Genome Atlas and Project should help in this regard, but they’re young projects, larger in scope than this work, and don’t necessarily integrate DNA changes with gene expression as do the investigators in this report. What’s clear, now, is that some cancers do respond, dramatically, to drugs that target specific mutations. Recently-incurable malignancies, like advanced melanoma and GI stromal tumors, can be treated now with pills, often with terrific responses.

Last night I wondered if, in a few years, some breast cancers might be treated without surgery. If we could do a biopsy, check for the molecular subtype, and give patients the right BC tablets. Maybe we’d just give just a tad of chemo, later, to “mop up” any few remaining or residual or resistant cells. The primary chemotherapy might be a cocktail of drugs, by mouth. It might be like treating hepatitis C, or tuberculosis or AIDS. (Not that any of those are so easy.) But there’d be no lost breasts, no reconstruction, no lymphedema. Can you imagine?

Even if just 1 or 2 of these investigators’ subgroups pans out and leads to effective, Gleevec-like drugs for breast cancer, that would be a dream. This can’t happen soon enough.

With innovative trial strategies like I-SPY, it’s possible that for patients with particular molecular subgroups could be directed to trials of small drugs targeting some of the pathways implicated already. The pace of clinical trials has been impossibly slow in this disease. We (and by this I mean pharmaceutical companies, and oncologists who run clinical trials, and maybe some of the BC agencies with funds to spend) should be thinking fast, way ahead of this post –

And given that this is a blog, and not an ordinary medical publication or newspaper, I might say this: thank you, authors, for your work.

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A JAMA Press Briefing on CER, Helicopters and Time for Questions

This week the Journal of the American Medical Association, JAMA, held a media briefing on its current, Comparative Effectiveness Research (CER) theme issue. The event took place in the National Press Club. A doctor, upon entering that building, might do a double-take waiting for the elevator, curious that the journalists occupy the 13th floor – what’s absent in some hospitals.

CER is a big deal in medicine now. Dry as it is, it’s an investigative method that any doctor or health care maven, politician contemplating reform or, maybe, a patient would want to know. The gist of CER is that it exploits large data sets – like SEER data or Medicare billing records – to examine outcomes in huge numbers of people who’ve had one or another intervention. An advantage of CER is that results are more likely generalizable, i.e. applicable in the “real world.” A long-standing criticism of randomized trials – held by most doctors, and the FDA, as the gold standard for establishing efficacy of a drug or procedure – is that patients in research studies tend to get better, or at least more meticulous, clinical care.

The JAMA program began with an intro by Dr. Phil Fontanarosa, a senior editor and author of an editorial on CER, followed by 4 presentations. The subjects were, on paper, shockingly dull: on carboplatin and paclitaxel w/ and w/out bevacizumab (Avastin) in older patients with lung cancer; on survival in adults who receive helicopter vs. ground-based EMS service after major trauma; a comparison of side effects and mortality after prostate cancer treatment by 1 of 3 forms of radiation (conformal, IMRT, or proton therapy); and – to cap it off – a presentation on PCORI‘s priorities and research agenda.

I learned from each speaker. They brought life to the topics! Seriously, and the scene made me realize the value of meeting and hearing from the researchers, directly, in person. But, NTW, on ML today we’ll skip over the oncologist’s detailed report to the second story:

Dr. Adil Haider, a trauma surgeon at Johns Hopkins, spoke on helicopter-mediated saves of trauma patients. Totally cool stuff; I’d rate his talk “exotic” – this was as far removed from the kind of work I did on molecular receptors in cancer cells as I’ve ever heard at a medical or journalism meeting of any sort –

Haider indulged the audience, and grabbed my attention, with a bit of history:  HEMS, which stands for helicopter-EMS, goes back to the Korean War, like in M*A*S*H. The real-life surgeon-speaker at the JAMA news briefing played a music-replete video showing a person hit by a car and rescued by helicopter. While he and other trauma surgeons see value in HEMS, it’s costly and not necessarily better than GEMS (Ground-EMS). Helicopters tend to draw top nurses, and they deliver patients to Level I or II trauma centers, he said, all of which may favor survival and other, better outcomes after serious injury. Accidents happen; previous studies have questioned the helicopters’ benefit.

The problem is, there’s been no solid randomized trial of HEMS vs. GEMS, nor could there be. (Who’d want to get the slow pick-up with a lesser crew to a local trauma center?) So these investigators did a retrospective cohort study to see what happens when trauma victims 15 years and older are delivered by HEMS or GEMS. They used data from the National Trauma Data Bank (NTDB), which includes nearly 62,000 patients transported by helicopter and over 161,000 patients transported by ground between 2007 and 2009. They selected patients with ISS (Injury severity scores) above 15. They used a “clustering” method to control for differences among trauma centers, and otherwise adjusted for degrees of injury and other confounding variables.

“It’s interesting,” Haider said. “If you look at the unadjusted mortality, the HEMS patients do worse.” But when you control for ISS, you get a 16% increase in odds of survival if you’re taken by helicopter to a Level I trauma center. He referred to Table 3 in the paper.  This, indeed, shows a big difference between the “raw” and adjusted data.

In a supplemental video provided by JAMA (starting at 60 seconds in):

When you first look, across the board, you’ll see that actually more patients transported by helicopter, in terms of just the raw percentages, actually die.” – Dr. Samuel Galvagno (DO, PhD), the study’s first author.

The video immediately cuts to the senior author, Haider, who continues:

But when you do an analysis controlling for how severely these patients were injured, the chance of survival improves by about 30 percent, for those patients who are brought by helicopter…

Big picture:

What’s clear is that how investigators adjust or manipulate or clarify or frame or present data – you choose the verb – yields differing results. This capability doesn’t just pertain to data on trauma and helicopters. In many Big Data situations, researchers can cut information to impress whatever point they choose.

The report offers a case study of how researchers can use elaborate statistical methods to support a clinical decision in a way that few doctors who read the results are in a position to grasp, to know if the conclusions are valid, or not.

A concluding note –

I appreciated the time allotted for Q&A after the first 3 research presentations. There’s been recent, legitimate questioning of the value of medical conferences. This week’s session, sponsored by JAMA, reinforced to me the value of meeting study authors in person, and having the opportunity to question them about their findings. This is crucial, I know this from my prior experience in cancer research, when I didn’t ask enough hard questions of some colleagues, in public. For the future, at places like TEDMED – where I’ve heard there was no attempt to allow for Q&A – the audience’s concerns can reveal problems in theories, published data and, constructively, help researchers fill in those gaps, ultimately to bring better-quality information, from any sort of study, to light.

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Illness is Not Discrete. On Feeling Sick, and Not Knowing What’s Next

This post is probably a bad idea. But I’ve been pondering it for two days now, since the room around me starting spinning. And I wish I were Jack Kerouac now, so that it wouldn’t matter so much if my thoughts are clear but that I tapped them out. Rat tat tat. Or Frank Sinatra with a cold. You’d want to know either of those guys, in detail. Up-close, loud, even breathing on you. You’d hire ‘em. Because even when they’re down, they’re good. Handsome. Cool, slick, unforgettable. Illness doesn’t capture them, or define them.

Two days ago I was feeling great. I went to the National Press Club for the first time, and was excited about some presentations I heard there, about which I took careful notes and intend, eventually, to share with some commentary. It was a sunny day, and I bought some groceries, planning a bunch of posts and to finish a freelance piece. In the evening I had dinner with my husband, and it seemed like my life was on track.

The rash was the first thing. Just some red, itchy bumps on the back of my neck. And then fatigue. Not just a little tired, but like I couldn’t write a sentence. And since then I’ve been in the center of a kaleidoscope, everything moving clockwise around my head. It’s not bright purple or hot pink and blue and stained glass-green kinds of colors circling, but the drab objects in the bedroom: the lamp, the shadow cast by the top of the door, the rows of light through the blinds, the brown and beige sheets, the back cover of last month’s Atlantic and my reading glasses on the nightstand, the gray bowl I’ve placed at hand, just in case I barf again. Walking is tricky. I’m dehydrated and weak, and my vision’s blurred.

This is not a pretty scene, if you could see it. And that’s the thing. The point.

Because in my experience, which is not trivial, people on both sides of illness – professionals and people you just know – are drawn to healthy people. A broken arm, a low-stage breast cancer that’s treated and done with, a bout of pneumonia – these are things that a career can afford, an editor can handle, friends can be supportive. But when you have one thing, and then another, and then another, it gets scary, it weighs you down. Just when you start feeling OK, and confident, something happens and you’re back, as a patient.

Today, in the apartment on this spring day, with fever and fatigue, I’ve got no choice. I am not a consumer now. Not even close. That is my role, maybe, when I go to the dentist and decline having x-rays or my teeth whitened. No choice, except if I go to a hospital, to have a bunch of blood drawn and my husband would fill in the forms before the doctors who don’t know me in this city inform me I’ve got a viral infection, and labrynthitis as I’ve had a dozen times before, all of a sudden, disabling. Nothing to do but rest and hydrate. And wish I’d gotten some other work done, but I couldn’t.

I’ve got to go with it, my health or illness, be that as it is. No careful critiques of comparative effectiveness research today. No reading about the Choosing Wisely guidelines. No post on Dengue, as I’d planned for yesterday.  Like many people with illnesses – and many with far more serious conditions – I’m disappointed. Maybe because I was sick as a child and missed half of tenth grade, I have trouble accepting these kinds of disruptions. Illness represents a loss of control, besides all the physical aspects.

I might try to watch TV, but more likely I’ll just fall sleep again. That happened yesterday. And for those of you health IT or gadget guys reading, who talk about smart phones and how useful they are for patients seeking info, or maybe even checking vitals, I’ll say this: I’m just glad I’ve got such a device, simply that I can call for help, that I can be in touch,  call my doctor and family. That makes being sick less scary.

This is a drag of a post, but it’s real. No point in blogging if I don’t say it like it is, what I am. If nothing else, this proves I’m alive. So there!

Better tomorrow –

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The Outlier’s Message, and Evolutionary Science in Breast Cancer

This past week I read several attitude-altering articles about breast cancer.

Kathy Rich, as featured in ‘O’ Magazine

The first lesson, if I might call it that – in the way an oncologist can learn from variations in her patients’ pathology and clinical outcomes – comes from the case of Katherine Russell Rich, who died last week at the age of 56. As reported by Katherine O’Brien in the I Hate Breast Cancer Blog, Rich lived with metastatic BC (MBC) for 18 years. That’s phenomenal, was my first reaction to this news. The prognosis for MBC is said to be around 3 years, and Rich lived for 18 years beyond her tumor’s recurrence with stage IV disease.

As sad and unsatisfactory as this outcome may seem, and it is, Rich’s story offers hope for life beyond the 3 or 4 or 5 years some women with MBC pray, “ask” or otherwise bargain for, fingers-crossed…

As she detailed in an O article, Rich’s initial diagnosis came when she was 32 years old, in 1988. The Times, in an obituary, tells of her lumpectomy, chemo and radiation. In 1993 her cancer came back in bones including her spine. She had a bone marrow transplant, but the disease progressed. Ultimately, she coursed through various and some archaic hormone treatments.

Along the way, she lost or quit a job in publishing, or both, and traveled to India, and authored two books. In a 2010 first-person story about her case, she told of updating her status – of being alive – at Breastcancer.org each year. She wrote:

…I tell the women how deeply I believe there’s no such thing as false hope: all hope is valid, even for people like us, even when hope would no longer appear to be sensible.

Life itself isn’t sensible, I say. No one can say with ultimate authority what will happen — with cancer, with a job that appears shaky, with all reversed fortunes — so you may as well seize all glimmers that appear.

My take, as an oncologist and former clinician, is that patients sometimes surprise you. Hard to know which woman will respond to a non-targeted treatment, or even a drug like an estrogen modulator, without trying. And I wonder – and this is speculative, but maybe, likely, the two together – doctor and patient – worked together to see what worked in Rich’s case over nearly 2 decades, and what didn’t work.

A Bell Curve

If a drug helps, keep it going; if it hurts, stop. There are so many algorithms in medicine, and molecular tools, but maybe the bottom line is how the, one, your patient is doing.

Which leads me to another post, by Dr. David Gorski, a breast cancer surgeon and researcher who blogs as Orac – what once was imagined as a fabulously capable information processor, at Respectful Insolence. He describes how tough it can be to define, and thereby target or destroy, any individual patient’s breast tumor because the cancer cells are constantly changing. Within each woman’s tumor, an evolution-like process is ongoing, leading to selection of treatment-resistant cells. This is not news in oncology; the concept has been understood for years as it applies to “liquid” tumors like leukemia, as he points out.

In breast cancer, understanding the complexity of each case is more recent. Gorski considers a genetic analysis of 104 triple negative breast cancer (TNBC) cases presented at the recent AACR meeting and published last week in Nature:

“…The 59 scientists involved in this study expected to see similar gene profiles when they mapped on computer the genomes of 100 tumours.

But to their amazement they found no two genomes were similar, never mind the same. “Seeing these tumours at a molecular level has taught us we’re dealing with a continuum of different types of breast cancer here, not just one,” explains Steven Jones, co-author of this study.

…TNBC is not a single disease. In fact, even an individual TNBC tumor is not a single disease. Tumor cells evolve as they proliferate, so that the cells in them are genetically heterogeneous. The cells growing in one area of a tumor can and often do harbor markedly different genetic mutations from the cells growing in another part of the tumor…

The team found that each tumor displayed multiple “clonal genotypes,” suggesting that the cancer would have to be treated as multiple diseases, rather than a single entity.

So besides that there are distinct subtypes of breast cancer, those labeled as TNBC are diverse and contain variation within; each patient harbors sub-clones of malignant cells that, in principle, respond differently to treatment.

Orac, the fictional supercomputer (Wiki-Commons image)

Putting these links together –

The message from Katherine O’Brien, who lives with MBC and blogs about it, is that one outlier – like Katherine Russell Rich – can provide hope to other patients and, maybe, clues for scientists about why she lived for so long with metastatic BC. The message from Orac, a physician-scientist blogger, is how hard it is to pinpoint an individual breast tumor’s molecular aspects, because the disease is so mutable and diverse.

The problem, and this reflects evolution in my thinking over a long while, is that published data – the gold standard, what supports EBM – are largely limited to findings based on trials of groups. We understand now, better than we did 10 or 20 years ago, that each patient’s tumor is unique and can evolve over time, naturally or in response to therapy. Clinical trials, though rigorously planned and elaborately structured, are incredibly expensive and flip-floppy, disappointing overall.

What I’m thinking –

Algorithms – except in the broadest sense – may not offer the optimal approach to cancer treatment. Maybe the median doesn’t matter so much as we’d thought.

Here’s a ~retro idea: In 2012, maybe the ideal and most cost-effective oncology practice would blend low-tech observations – like findings on physical examination and how the patient’s feeling – with occasional, high-tech analyses – like markers for genetic drift within a tumor. If doctors are well-trained and non-robotic, in either the literal or figurative sense, and if they lack COIs regarding treatment decisions, they’d provide better, more effective and personalized treatments than what’s typically offered based on evidence reached through elaborate, costly clinical trials of many patients with similar but non-identical cancers.

All for now,


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New Article on Mammography Spawns False Hope That Breast Cancer is Not a Dangerous Disease

This week’s stir comes from the Annals of Internal Medicine. In a new analysis, researchers applied complex models to cancer screening and BC case data in Norway. They estimated how many women found to have invasive breast cancer are “overdiagnosed.” I cannot fathom why the editors of the Annals gave platform to such a convoluted and misleading medical report as Overdiagnosis of Invasive Breast Cancer Due to Mammography Screening: Results From the Norwegian Screening Program. But they did.

Here are a few of my concerns:

1. None of the four authors is an oncologist.

2. The researchers use mathematical arguments so complex to prove a point that Einstein would certainly, 100%, without a doubt, take issue with their model and proof.

3. “Overdiagnosis” is not defined in any clinical sense (such as the finding of a tumor in a woman that’s benign and doesn’t need treatment). Here, from the paper’s abstract:

The percentage of overdiagnosis was calculated by accounting for the expected decrease in incidence following cessation of screening after age 69 years (approach 1) and by comparing incidence in the current screening group with incidence among women 2 and 5 years older in the historical screening groups, accounting for average lead time (approach 2).

No joke: this is how “overdiagnosis” – the primary outcome of the study, is explained. After reading the paper in its entirety three times, I cannot find any better definition of overdiagnosis within the full text. Based on these manipulations, the researchers “find” an estimated rate of overdiagnosis attributable to mammography between 18 -25% by one method (model/approach 1) or 15-20% (model/approach 2).

4. The study includes a significant cohort of women between the ages of 70-79. Indolent tumors are more common in older women who, also, are more likely to die of other causes by virtue of their age. The analysis does not include women younger than 50 in its constructs.

5. My biggest concern is how this paper was broadcast – which, firstly, was too much.

Bloomberg News takes away this simple message in a headline:  “Breast Cancer Screening May Overdiagnose by Up to 25%.” Or, from the Boston Globe’s Daily Dose, “Mammograms may overdiagnose up to 1 in 4 breast cancers, Harvard study finds.” (Did they all get the same memo?)

The Washington Post’s Checkup offers some details: “Through complicated calculations, the researchers determined that between 15 percent and 25 percent of those diagnoses fell into the category of overdiagnosis — the detection of tumors that would have done no harm had they gone undetected.” But then the Post blows it with this commentary, a few paragraphs down:

The problem is that nobody yet knows how to predict which cancers can be left untreated and which will prove fatal if untreated. So for now the only viable approach is to regard all breast cancers as potentially fatal and treat them with surgery, radiation, chemotherapy or a combination of approaches, none of them pleasant options…

This is simply not true. Any pathologist or oncologist or breast cancer surgeon worth his or her education could tell you that not all breast cancers are the same. There’s a spectrum of disease. Some cases warrant more treatment than others, and some merit distinct forms of treatment, like Herceptin, or estrogen modulators, surgery alone…Very few forms of invasive breast cancer warrant no treatment unless the patient is so old that she is likely to die first of another condition, or the patient prefers to die of the disease. When and if they do arise, slow-growing subtypes should be evident to any well-trained, modern pathologist.

“Mammograms Spot Cancers That May Not Be Dangerous,” said WebMD, yesterday. This is feel-good news, and largely wishful.

A dangerous message, IMO.

Addendum, 4/15/12: The abstract of the Annals paper includes a definition of “overdiagnosis” that is absent in the body of the report: “…defined as the percentage of cases of cancer that would not have become clinically apparent in a woman’s lifetime without screening…” I acknowledge this is helpful, in understanding the study’s purpose. But this explanation does not clarify the study’s findings, which are abstract. The paper does not count or otherwise directly measure any clinical cases in which women’s tumors either didn’t grow or waned. It’s just a calculation. – ES

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