Home >> ALL ISSUES >> 2018 Issues >> Time now for tumor mutational burden?

Time now for tumor mutational burden?

image_pdfCreate PDF
Karen Titus

November 2018—Like a piece of so-called sticky music, cutoff numbers can persist in physicians’ minds outside of any real clinical value and, in the process, leave their laboratory colleagues mildly befuddled (not to mention searching for more useful cutoffs).

Such a jingle is creeping into tumor mutational burden. Lauren Ritterhouse, MD, PhD, co-director of the clinical genomics laboratory at the University of Chicago, recalls early conversations about TMB at her institution. Amid discussions about how and when to implement the testing, one colleague announced to all assembled that the cutoff number should be 100.

Dr. Lauren Ritterhouse at the University of Chicago, where the clinical genomics laboratory she co-directs has a research module for measuring tumor mutational burden. “Our data actually looks close to perfect,” she says. [Photo by Bruce Powell]

“I asked, ‘Do you mean 100 per exome?’” she recalls. The colleague was unsure—but kept repeating the number anyway: I don’t know. But I know it’s 100.

As it turns out, says Dr. Ritterhouse, who is also assistant professor of pathology, the figure had some basis in fact, if not usefulness. One of the initial publications on TMB measurements used 100 mutations/exome as a cutoff value, and that’s what stuck in her colleague’s head.

More recently, another number has emerged, based on the use of the FoundationOne CDx assay (Foundation Medicine) in a recent clinical trial. “My colleagues are seeing the number 10—that’s a number that’s been thrown around lately. But it’s just a number,” Dr. Ritterhouse says.

Likewise, TMB is just a test—but one drawing more attention for its potential use as a predictive biomarker in immunotherapy, particularly in non-small cell lung cancer, melanoma, and urothelial cancers.

The idea is basic, says Alain Borczuk, MD: Tumors eventually learn to evade the immune system following initial attacks. But subsequent immune system response may be more robust in cases where the tumor expresses neoantigens on the cell surface, says Dr. Borczuk, vice chair of anatomic pathology and professor of pathology, Weill Cornell Medicine. When the immune system is reactivated by immuno-oncology drugs, the response will be better if the tumor is more immunogenic; TMB measures the number of mutations within a tumor genome.

“It’s a theory,” says Dr. Borczuk. The cancer field is full of theories, of course. But this one has some support, he says, although it’s not uniformly recognized that TMB is the only measure of response to these drugs, also known as immune checkpoint inhibitors.

Different approaches have been used to try to predict response, including, most prominently, PD-L1, which has become the standard biomarker in a variety of tumor types. “Everyone recognizes it as an imperfect biomarker, but nevertheless it’s the one we have,” says Dr. Borczuk. Perhaps, goes the hope, TMB will be a better or potentially a different predictor for a set of patients who might respond to immunotherapy. This hope grew bigger on the basis of a study involving an examination of tumors that had microsatellite instability, or MSI, which seemed to indicate a better response to the immuno-oncology drug pembrolizumab, regardless of site of tumor origin.

TMB testing is ‘not going away. It is a piece of data that people will want.’
Alain Borczuk, MD

With TMB thus nudged into the spotlight, compelling data have emerged, primarily related to NSCLC, specifically from the CheckMate 227 study, which looked at TMB and PD-L1 in patients who were offered either chemotherapy, an anti-PD-1 drug, or an anti-PD-1 drug in combination with an anti-CTLA-4 drug.

Reports Dr. Borczuk: “What they found—and this is the data that has been most discussed—is that patients who had high tumor mutation burden had a longer progression-free survival and a longer duration of that response with combination immunotherapy, when compared to chemotherapy.” It was, he says, “the type of difference that really captures your eye.” One-year progression-free survival with nivolumab plus ipilimumab was 42.6 percent, versus 13.2 percent with chemotherapy. Median progression-free survival was 7.2 months versus 5.5 months.

The study set the bar, he says, for TMB in predicting the subset of patients who could potentially respond to combination immunotherapy, especially since it seemed to be independent of the PD-L1 level. A second study, also looking at NSCLC, again found that higher TMB seemed to be a predictor of higher response rates to immuno-oncology.

Now comes the wait to see if the CheckMate 227 results will nudge the FDA to approve combination immunotherapy based on the biomarker.

In the meantime, laboratories can ponder the “hows” of TMB testing. Dr. Borczuk has told his colleagues that developing the testing is a must. “It’s not going away,” he says. Even if the importance of this particular piece of data isn’t fully clear, “it is a piece of data that people will want.”

At the University of Chicago, such efforts are already underway. Dr. Ritterhouse’s lab currently runs a large (1,213 genes) targeted panel for its cancer specimens, sizable enough, she says, that they can easily bring on larger mutational pattern metrics like TMB.

But validation issues are fraught. The original study and data suggesting the usefulness of TMB in immunotherapy were done using tumor and normal whole exome sequencing studies, “so that’s still kind of seen by many as the gold standard,” Dr. Ritterhouse says. All well and good, but validating to this particular gold standard “means that however many samples you’re going to run, you have to pay for two exomes in addition to whatever your panel is.”

Her lab had a set of cases that had already been run on its in-house panel; on about 30 of them, they were able to obtain or already had additional DNA as well as normal tissue, on which they performed whole exome sequencing. WES isn’t a routine step for them, however, which meant “we had to spend a lot of time getting the pipeline and variant calls for those whole exome sequencing analyses.” In fact, she says, that turned out to be the trickiest part of obtaining an accurate TMB. “It’s easy to add up variants, but making sure you have accurate variant calls” is a different matter.

UC does tumor-only sequencing; matched-normal sequencing, she says, is more expensive, and the logistics are tricky, including a separate consenting process, sample access issues, and potentially longer turnaround times. Adding to the complexity, UC has a separate, nonpathology lab that does inherited germline testing to determine cancer predisposition. Given these many moving parts, she and her colleagues wanted to see how accurate a tumor-only approach would be. If the results weren’t good enough, they’d then look to matched-normal sequencing.

For now, there’s no need to look further. Dr. Ritterhouse says she’s pleased with the results so far, calling them “really nice. Our data looks good.” The huge panel it uses—a little over 3 megabases—helps. “I know a lot of labs that have much smaller panels are facing a tougher time. It’s hard to get an accurate sampling of what you’d get in a whole exome sequencing if you’re only looking at 50 or so genes.”

Adding to her confidence, she notes that Foundation Medicine’s test also uses tumor-only sequencing. “So it’s not unreasonable to think” it’s a good approach, she says, before adding, “Although no one really knows how they filter their germline variants. They have a proprietary algorithm that’s used, and you can make guesses as to how they do it, but it’s a bit of a black box.”

It’s a point of consternation, to be sure. “We would all love to know—it would be helpful to many, many labs. But.” She pauses before uttering a pragmatic, Zen-like phrase that seems to burble up when pathologists talk about TMB: “This is the setting we’re working in.”

At UC, “We’re still struggling with how to best filter out germline SNPs,” Dr. Ritterhouse says. Like other labs, “We use population databases, but you know those aren’t perfect. You’re going to throw out some variants that are somatic, and then you’re going to miss a lot of private inherited SNPs that a patient might have.”

Currently the laboratory has a research module for measuring TMB, but the clinical launch may be another six to nine months away.

What have been the challenges so far? “One is coming up with the data you want to test,” says Dr. Ritterhouse. UC essentially did 60 whole exomes. But, she says, many labs might find that financially daunting. Some colleagues with whom she has spoken are choosing instead to validate against samples they already had in their own institution that had been tested at Foundation Medicine.

Not having an existing informatics pipeline for whole exome sequencing added to UC’s challenges, she continues; most cancer labs, in fact, don’t regularly do whole exome sequencing. There’s a lot of pseudogene noise, along with artifacts and signals. The laboratory will need to devote a large chunk of time (“months and months and months” is how she adds it up) making a WES bioinformatics pipeline, or have access to someone else’s.

Even then, it’s tricky. In filtering out the germline variants, she and her colleagues took a strict approach. “But it ends up not working so well on MSI cases and high-TMB cases. We found that a lot of the actual somatic variants are getting thrown out.” It was “almost perfect for everything below 20 mutations per megabase,” however.

The issue hasn’t been fully resolved. “We’re trying to come up with new ways to get rid of the germline variants that won’t penalize some of these high-TMB cases,” Dr. Ritterhouse says. Options include looking at inherited, common SNPs and variant allele frequency. “If they’re adjacent to your variant, then you can make some Bayesian analyses as to whether this might be inherited or somatic.”

Apart from this ongoing frustration of tossing out too many germline variants, she sounds pleased. “Everything else looks so beautiful,” she says, bringing an artist’s appreciation to the results. “Our data actually looks close to perfect.”

At Brigham and Women’s Hospital and the Dana-Farber Cancer Institute, Jonathan Nowak, MD, PhD, and colleagues have been running a targeted sequencing panel since 2013 that has covered, in different iterations, about 300 to, now, some 450 genes. Over the past year, they’ve used the data generated from this panel, along with looking at mutational patterns, to make calls on mismatch repair status across all tumor types, which became important when pembrolizumab was approved for advanced, MMR-deficient tumors in 2017.

In concert with deploying MMR analysis, “We also realized that another very worthwhile thing to measure would be tumor mutational burden,” says Dr. Nowak, associate pathologist at Brigham and Women’s and instructor of pathology, Harvard Medical School. “We know, of course, that MMR deficiency correlates reasonably well with an elevated TMB, although many tumors that have an elevated mutational burden aren’t MMR deficient.”

So in mid-2017, the lab also began reporting TMB for every tumor sequenced on its panel—“pretty much every solid tumor we see at the Dana-Farber, and some hematologic malignancies as well.”

Early on, he says, they struggled with the best way to report. Providing only a number would have limited utility, he says, especially for uncommon tumors.

The solution? They built a reporting module that presents the TMB for the current tumor not only as the number of mutations per megabase but as a percentile, comparing it to previously sequenced tumors of the same type. “If we are sequencing our 500th colon cancer, for example, we’d be able to say that the mutational burden is 12, and that’s in the top 86th percentile of all colon cancers we’ve sequenced so far.” Additionally, tumors are also compared at a percentile level to all cases, regardless of tumor type, previously sequenced by the panel, which helps provide a context for TMB results in uncommon tumor types.

Figuring out how to report and classify results “is, honestly, almost the hardest part of this work,” Dr. Nowak says. For institutions that plan to offer TMB, “that’s an open question.” Not everyone has the resources of the Dana-Farber, he acknowledges. “Our situation is unusual.”

Yes, it is, says Dr. Ritterhouse. “For them, it’s a fantastic way to do it. We can provide those numbers,” but lacking a vast database of their own, “they won’t have as much power.” Many laboratories, she says, simply report a number and sidestep the larger issue. Interpretation could depend on tumor type or drug therapy and the combination being considered. UC hasn’t yet decided how it will report.

As for the calculation, Dr. Nowak notes that almost every step of the bioinformatics pipeline as well as preanalytic variables can interact and cumulatively influence the TMB number. For laboratories that do whole exome sequencing, calculating TMB is “pretty trivial. Because you’ve sequenced all the genes, it takes some variables off the table,” he says. But most labs will probably use a targeted panel, since this provides deeper coverage and faster turnaround times. But that raises another question: How concordant must those results be to WES? “Is it OK if you’re within five or 10 percent of the TMB as estimated by whole exome sequencing?” he asks.

The other challenges are typical of any assay, including having an adequate sample. “What we have generally found,” says Dr. Nowak, “is if there is an adequate amount of DNA available to perform our sequencing assay, it’s not a challenge to calculate TMB, as long as the tumor content of the specimen also meets our threshold. For instance, our validation studies show that we need 50 nanograms of DNA from a specimen with at least 20 percent tumor content. We know from these validation studies that if we have a specimen that meets both of those criteria, we can reproducibly generate the same sequencing results for that specimen.”

“You can always run into trouble if you have an inadequate or borderline specimen,” Dr. Nowak continues. “But I think the other big challenge for TMB is ensuring that there is sufficient tumor content for whatever sequencing depth your panel provides.” This varies from institution to institution and even within a single lab, depending on whether the lab performs amplicon-based or hybrid capture-based sequencing. In a specimen with too little tumor, despite an adequate amount of DNA, “you might end up with an artificially low TMB.”

CAP TODAY
X