Andrew D. Smith

In 2014, three years after the artificial intelligence system that IBM calls Watson thrashed two humans in a game of “Jeopardy!”, the company began selling Watson’s services as a virtual oncologist, an intelligent machine that would look at patient records and make treatment recommendations.

Some predicted that Watson would clobber oncologists for much the same reason it bested game show contestants —it could retain more data than any person. Before recommending treatment, it could “remember” every word from every cancer study ever published and consider every finding that applied to a particular patient.

In reality, according to a 2017 investigation published by the news website Stat, Watson struggled “with the basic step of learning about different kinds of cancers” and disappointed many of the hospitals and practices that purchased its services, sometimes by recommending “unsafe and incorrect” cancer treatments.

Watson is likely the best-known example of artificial intelligence, or AI, scanning big data to achieve “deep learning” in complex subjects — meaning a computerized system that analyzes enormous stores of information and recognizes patterns from the data on its own. The struggles of the IBM system illustrate a tendency to overestimate technologies. Yet lesser-known exercises in big data analysis have improved the care that many patients with cancer receive, and a string of promising study results and new business openings suggest that AI could soon play a significant role in both of the most vital parts of cancer care: finding the cancer and determining the treatment.

“AI is never going to fully replace physicians, but researchers have made impressive strides in developing AI that specializes in particular tasks, like reading images from CT scans or pathology slides,” says Ryan Schoenfeld, vice president of scientific research at the Mark Foundation for Cancer Research, a New York City-based philanthropy. “I expect we will continue to see further advances in this area.”

The Food and Drug Administration (FDA) has already cleared at least one product that uses AI to analyze mammography. The Transpara system, from a Dutch company called ScreenPoint Medical, has performed well in a wide variety of tests. The biggest challenge pitted Transpara against 101 radiologists in the analysis of 2,652 digital mammograms and found that the AI matched the average performance of the humans.

Such results suggest that ScreenPoint’s AI, acting alone, would already do a better job reading mammograms than about half of all radiologists, but the FDA has cleared the system to help these doctors, not to replace them.

The South Korean government has approved a similar mammogram-analysis system from Lunit. The company also sells Lunit Insight CRX, an AI service that can search for lung cancer in CT scans and is used by hospitals in Mexico, Dubai and South Korea.

Another AI system that helps radiologists detect lung cancer in CT scans comes from an Amsterdam-based company called Aidence. That system, dubbed Veye Chest, is approved for use in the EU and is already deployed at 10 hospitals in the Netherlands, the U.K. and Sweden. Here in the U.S., a number of organizations are developing cancer-spotting AI systems, many of which have also performed well in trials.

Earlier this year, researchers from Google compared their AI against six radiologists in detecting lung cancer from CT scans. The model, which had learned to spot lung cancer by examining 45,856 CT scans from the National Cancer Institute (NCI) and Northwestern University, matched the doctors in cases where both person and machine examined current and older CT scans of the same patient. When no historical images were available and diagnosis hinged entirely on images taken on a single day, the AI beat the physicians, producing 11% fewer false positives and 5% fewer false negatives.

That success came less than a year after Google completed two tests of an AI system designed to spot evidence of breast cancer metastases in images of biopsied lymph nodes. Finding such metastases is a notoriously difficult task for humans. Previous research found that the detection of small metastases on individual slides can be as low as 38%. Google’s tool, Lymph Node Assistant (LYNA), fared considerably better. In the first test, LYNA was able to distinguish a slide with cancer from a cancer-free slide 99% of the time. It was also able to accurately determine the location of the cancers, even those that were too small to be consistently identified by pathologists. In the second study, Google compared how pathologists performed with and without help from LYNA and found that artificial assistance not only improved accuracy but also halved the amount of time needed to review each slide.

Google isn’t the only large organization that’s developing or testing such technology. Many of the nation’s major cancer centers are working with universities, high-tech companies in Silicon Valley or both to develop AI that can help radiologists and pathologists spot tumors faster and more reliably. They even hold competitions to encourage boldness and innovation.

“Machine learning is a very powerful tool, but it requires a huge amount of data,” says Tony Kerlavage, who runs one of the nation’s largest repositories for cancer data, the NCI’s Center for Biomedical Informatics and Information Technology. “Researchers have struggled in the past find enough data, all in the same place, all in the same format, and that has hindered progress. Our goal is to make truly massive amounts of quality data accessible to everyone.”

Such efforts, if successful, would be great news for patients, because there’s plenty of room for improvement in analyzing pathology mammograms and CT scans. Current mammography practices fail to see about 20% of all breast cancers (false-negative results) and frequently identify healthy tissue as cancerous (false-positive results): About half of women who get annual mammograms for 10 years will receive at least one false positive. Lung cancer screening, which is typically done via low-dose computed tomography scans, may produce exponentially more false positives than true positives. In a study of 2,106 Veterans Affairs patients who underwent recommended lung cancer screening, radiologists identified nodules that required follow-up in 1,184 patients, but just 31 had lung cancer.

The term “big data” was coined in the 1990s to describe a deluge of information made newly available by two major changes. First, a wide range of things that had once been recorded exclusively on paper, film or magnetic tape — along with a variety of information that had never been stored &mdash; started being saved on computers. Second, those computers, which had once been as isolated from the outside world as file cabinets, were getting hooked up to company intranets and the internet.

Big data is so big that the numbers are hard to fathom. Former Google CEO Eric Schmidt estimated that by 2010, people were producing as much searchable information every two days as mankind had collectively produced from the dawn of civilization until 2003. This includes social media, such as tweets and instant messages that may not contribute to scientific progress, but the data with scientific meaning has proved very valuable.

Among the most important information produced early in the big data era came from the Human Genome Project (HGP) and the private-sector equivalent undertaken by Celera Genomics. The HGP took 13 years and $3 billion to sequence the 3.3 billion DNA pairs in a complete genome, and the dividends were huge. Before the HGP, researchers had found genetic causes or risk factors for about 60 conditions. A decade after its completion, they upped that figure to about 5,000, and the number of drugs for which use was at least partly governed by genetic testing exceeded 100.

These advances have been particularly helpful for patients with cancer. The FDA has approved more than 50 genetically targeted cancer therapies, and researchers are working on countless others. Oncologists should already be ordering genetic tests on many if not most tumor samples and using results to guide treatment. Genetic tests and targeted medications constitute a very real benefit for patients, but they don’t represent the truly revolutionary leap that optimists promised at the beginning of the big data age.

The hope then was that medical practices would quickly transition to electronic health record (EHR) systems that would facilitate significant breakthroughs by letting researchers analyze many factors associated with real-world medical outcomes. The reality proved disappointing,

and not just because doctors resisted the move to time- consuming EHRs the way most kids resist eating vegetables.

Data analysis has traditionally required what is called structured data — i.e., data entered in exactly the right box in exactly the right way. Spreadsheets and databases could not analyze handwritten notes, digital images, PDFs of test results or any of the types of unstructured data that EHRs stored.

The only way to make some of that information useful was to pay humans to analyze unstructured text, convert it into the proper words and enter it into the proper fields. Fortunately, software such as natural language processing (think Amazon’s Alexa) is starting to make things easier — but just a little. Meanwhile, the digitization of radiology and pathology results has greatly facilitated the development of AI strategies.

“We are now using AI to help with data collection, but the technology isn’t to the point where you click a button and you get all the information reliably extracted from source material and entered properly in our database. We’re not even at that point with plain but unstructured text, let alone PDFs or images, but we are getting closer,” says Dr. Robert S. Miller, medical director of CancerLinQ, a nonprofit subsidiary of the American Society of Clinical Oncology (ASCO) that collects data from participating members.

CancerLinQ is primarily designed as a quality improvement tool. It allows practices to compare how they stack up against each other, identify where they fall short of their peers or fail to follow existing standards of care, and make improvements. This is an unusual strategy for using big data to improve cancer care, and Miller says it has helped participating practices identify areas where they can make significant improvements.

About 100 of the nation’s more than 2,200 hematology/oncology practices have signed up for CancerLinQ, and about half of those are actively participating. Dozens of additional practices have expressed interest in the program, and Miller expects participation to increase sharply now that the information that practices submit can also be used, with no further effort, to qualify for ASCO’s Quality Oncology Practice Initiative certification program.

In addition to helping practices improve their operations, CancerLinQ has begun supplying researchers with data they can analyze, and researchers were quick to take advantage. ASCO’s last annual meeting saw the presentation of five studies that mined CancerLinQ.

One study investigated outcomes among patients with lung cancer who used checkpoint inhibitors despite prior autoimmune disease. The findings revealed that these patients fared about as well as similarly treated patients with lung cancer but no autoimmune disease. It was a potentially important finding because patients with autoimmune disease were excluded from most checkpoint inhibitor trials, so there was little published information about how they respond to these medications.

“We are looking to expand the data that CancerLinQ can track to include more genetic information about both tumor and patient. That will be a challenge because many practices store that data in their EHR as PDFs or even badly photocopied faxes, but we are investigating ways to get structured data from the sequencing labs themselves or from those EHRs that do get data directly from the lab,” Miller says. “As we bring in more molecular data, the chance of seeing unexpected associations with important implications will increase.”

One of the most important implications that a study can uncover is that treatment guidelines called standards of care should be changed for a particular patient population.

Expert committees of organizations such as ASCO and the National Comprehensive Cancer Network periodically review all the current research on each tumor type and tell doctors how to treat every subtype at every stage in the tumor’s progression. Some guidelines strongly recommend a single treatment that works well for nearly every patient, such as the complete surgical removal of a cancer that has yet to spread. In many other cases, guidelines give equal recommendation to multiple treatments, each of which will work in some patients but not in others. Doctors and patients must discuss each option, make a choice and hope they don’t waste valuable time on something that is ineffective.

That risk is particularly relevant when using immuno- therapies known as checkpoint inhibitors. These medications often get the highest recommendations because when they work, they often work far better than alternative medications. For most eligible patients, however, they produce no response.

Many research teams hope to discover the differences between responders and non-responders by analyzing mountains of data, either by running the analyses them- selves or teaching software to look for them. In one of the most promising efforts reported thus far, researchers mined data from a bladder cancer trial that collected an unusual amount of information about participants’ demo- graphics, medical history, tumor biology and immune cells.

The researchers gave their AI 36 points of information on every patient and asked it to devise an algorithm that would separate patients who responded from those who did not. The software eventually found 20 features that, when considered collectively and weighted appropriately, explained 79% of the response variation. Had that software’s algorithm been used to include and exclude trial patients, it would have included all patients who responded to treatment but just 38% of non-responders.

Such results, the study authors note, do not prove that the same algorithm would predict immunotherapy response just as accurately for all patients with cancer or even those with bladder cancer who resemble the study participants, but the findings do suggest that the key to predicting response lies in analyzing far more than the two or three factors that currently qualify patients for immunotherapy.

Another effort to increase response rates by using more data points to distinguish patients and personalize treatment is underway at Cambridge University in the United Kingdom. The team, which received an 8.6 million-pound grant from the Mark Foundation, wants to predict each cancer’s progression and which treatments will work best by integrating information from imaging, biopsies, blood tests and genetic tests.

“Cancer patients already undergo a number of diagnostic tests that collectively provide considerable information, but treatment decisions are usually guided by specific results on one test — like a HER2 mutation &mdash; or a general feel for the combined picture. There’s no way that doctors can currently make a decision based upon the exact combination of relevant factors on all those diagnostic tests. We plan to change that,” says Richard Gilbertson, director of the Cancer Research UK Cambridge Centre. “We have a huge amount of patient data here at Cambridge because we have a long history of clinical trials that collect thorough patient data. We also have some very talented mathematicians and computer scientists who can create the algorithms needed to separate signal from noise.”

The Cambridge team may not be testing its individualized treatment recommendations in live patients quite yet, but a team at Columbia University’s Califano Systems Biology Lab is doing just that with its N of 1 trial program.

In a typical trial of a drug, researchers enroll similar patients and give them all the same treatment. Columbia’s project starts with a variety of patients who have exhausted all their treatment options; finds the functional drivers, or “master regulators,” of each patient’s tumor; and looks for any drugs that may disrupt them — not just cancer drugs but also medications approved for any condition and experimental therapies that have reached late-stage trials. Researchers pit the proposed treatment against a tumor sample in the lab or in a mouse. These N of 1 trials do not require patients to take the medications, but, at their oncologist’s discretion and based on drug availability, some patients do try the drugs recommended by the computer.

“We’ve tested 39 drugs so far, and we have seen a remarkable response rate. There have been 60% of objective responses in mouse transplants so far, including both regression and stable disease,” says Andrea Califano, who runs the laboratory. “This is really a proof-of-concept trial, and the concept is that you can test a system for picking treatments rather than testing a particular treatment.”

Articles

Using artificial intelligence to analyze huge stores of information known as big data boosts the effectiveness of cancer diagnosis and treatment.

As Large As Life: Using Artificial Intelligence in Cancer Care

Is immunotherapy ready for prime time in breast cancer?

Elizabeth A. Mittendorf, associate professor of surgical oncology at The University of Texas MD Anderson Cancer Center in Houston, needs only a single word to answer this question: No.

The most advanced treatment candidates are just beginning later stage trials. That said, their performance so far suggests that immunotherapy may become a powerful tool against breast cancer for those oncologists who follow the research and prepare for its arrival.

Mittendorf differentiated the main types of immunotherapy and highlighted some of the most interesting results in breast cancer trials during her presentation at the 32nd Annual Miami Breast Cancer Conference. She also discussed why different types of immunotherapy might be appropriate for different types of breast tumors at various stages of development.

"Researchers have dreamed of turning the immune system against tumors for more than half a century now, and—after decades of incremental progress&mdash;the science has reached the sort of tipping point that has produced a large number of promising compounds," Mittendorf says.

Breast cancer specialists once assumed that the relative paucity of nearby T cells limited the potential of immunotherapy in their field, so they concentrated (very effectively) on targeted treatments. Patients with breast cancer have thus had limited access to the kinds of immunotherapies that are currently available to people with melanoma or prostate cancer. But that may change.

Immunotherapy development currently tends to focus broadly on three treatment categories: chimeric antigen receptor (Car) T-cell therapies, vaccines, and checkpoint blockade strategies. Car therapies, which coat the patient's T cells to help those cells recognize tumor antigens, remain largely untested against breast cancer, so Mittendorf focused on vaccines and checkpoint inhibitors.

For patients with breast cancer, the most advanced vaccine is NeuVax (nelipepimut-S). The formulation is a peptide derived from HER2 protein, combined with the immunoadjuvant granulocyte-macrophage colony-stimulating factor (GM-CSF). Patients receive it monthly for six months and then every six months thereafter for up to three years.

In a phase 1/2 trial as adjuvant therapy on 187 women with node-positive and high-risk node-negative tumors that expressed any degree of HER2, the five-year rate of disease-free survival (DFS) was 89.7 percent overall for those who received the vaccine compared with 80.2 percent for unvaccinated women.

For optimally dosed patients, the DFS rate rose to 94.6 percent.

The phase 3 PRESENT trial now underway seeks to randomize 700 women with early-stage, node-positive breast cancer with low to intermediate HER2 expression to NeuVax or an active comparator composed of the GM-CSF sargramostim (Leukine). To be eligible for the trial, patients must have primary tumor stage T1-3 at initial diagnosis that was completely excised through surgery or are receiving neoadjuvant therapy before surgery.

"Vaccination takes months to produce an immune response, so it's probably not an appropriate treatment strategy for very late-stage cancers, but it appears to be particularly appropriate in an adjuvant setting," says Mittendorf, who has been leading the NeuVax trials.

"At that point, when most patients are already disease-free, harsh treatments make no sense. The tradeoff between toxicity and benefit is not in the patient's favor. Effective vaccines, on the other hand, make perfect sense because they rarely subject patients to anything worse than mild pain or itching at the injection site. NeuVax is no more grueling than a flu shot."

There is also reason to think that many vaccines will prove more effective in the adjuvant setting than they have, to date, in the advanced-disease setting. Vaccines against infectious diseases rank among the most beneficial tools created by medical science, but they have a major limitation. They're far better at prevention than treatment. In general, the more a patient already suffers from the target disease at the time of vaccination, the less a vaccine helps the patient.

"We don't know enough yet to make blanket statements about the optimal timing for different immunotherapies, but anecdotal evidence suggests that vaccines are less effective in patients with advanced metastatic cancer," Mittendorf says. "It would explain why many of the earliest vaccine trials, which enrolled patients with diffusely metastatic tumors, failed to demonstrate benefit.

"Timing, in general, is critical in oncology, and it may prove particularly important with immunotherapy," she says. "Research needs to demonstrate not only whether treatments work but when and how they work the best so that clinicians can prescribe each patient the right treatment at the right time."

 they work best will eventually entail testing combinations of immunotherapies that, logically, should produce synergies, but testing to date has mostly involved supplementing existing standards of care with a single vaccine or, in other cases, a single checkpoint inhibitor.

Regulators have approved three such compounds to date: Yervoy (ipilimumab), Keytruda (pembrolizumab) and Opdivo (nivolumab). The first of those drugs makes it harder for tumors to call off immune attack via the cytotoxic T-lymphocyte-associated protein—(CTLA-4) pathway. The others reduce similar communication via the programmed death receptor-1 (PD-1) pathway.

At present, all three agents are approved exclusively for melanoma, but they may work against other tumors. Both of the anti-PD-1 medications are already in late-stage trials against certain lung cancers and early-stage trials against breast cancer.

A phase 1b trial of Keytruda in metastatic triple-negative breast cancer produced an overall response rate of 18.5 percent. The experimental compound MPDL3280 also produced responses in a similar patient group.

Such apparent effects may be explained, at least in part, by a finding from Mittendor's research group: 20 percent of all triple-negative breast tumors express the PD-1 ligand, PD-L1. PD-L1 expression levels are considered a potential biomarker for PD-1—targeted agents.

Given that triple-negative tumors respond so little to all existing treatments, significant progress against even 20 percent of them would be cause for celebration, but separate research suggests there are ways to increase PD-L1 expression on tumors and ways to boost the number of T cells available to attack the tumor.

"There are a lot of promising ideas for improving response rates and increasing the strength of those responses, though there is also concern that the relative lack of T-cell activity inside the breast will effectively prevent a large percentage of patients from responding to checkpoint blockade," Mittendorf says.

"Still, even if the latter idea proves right and response rates never exceed 20 percent, immunotherapy would still constitute a breakthrough in treatment. Only 20 percent of breast cancers express HER2 and nobody would call the treatments that target such tumors anything less than a breakthrough," she says.

Elizabeth A. Mittendorf, associate professor of surgical oncology at The University of Texas MD Anderson Cancer Center, discusses the progress and potential of immunotherapy in breast cancer treatment. 


Is Immunotherapy Ready for Prime Time in Breast Cancer?

With advances in screening strategies and targeted therapy for lung cancer, the arrival of immunotherapy looks to be the next big step in helping patients with the disease.

	Roy S. Herbst, chief of medical oncology at the Yale Cancer Center and Smilow Cancer Hospital at Yale-New Haven, Connecticut, led some of the first trials of Tarceva (gefitinib), the EGFR inhibitor that helped introduce targeted therapies of this important mutation into the treatment landscape of non-small cell lung cancer (NSCLC). But Herbst believes that many of today’s experimental immunotherapies will soon provide even greater benefit to even more patients. Herbst explained his reasoning during a presentation at the 9th Annual New York Lung Cancer Symposium.

	“The trial results for some of these therapies rank among the most exciting things I’ve ever seen,” Herbst said in an interview with OncologyLive. “Several of these treatments are almost certain to win rapid approval and quickly become the standard of care for a large percentage of all patients with lung cancer.”

	Herbst’s presentation covered both the theory behind immunotherapy and the performance to date of medications such as nivolumab and Keytruda (pembrolizumab).

	Lung cancer specialists are waiting eagerly for results from the phase 3 nivolumab trials, which are expected any time now, but early trial results explain Herbst’s optimism. Results from the CheckMate-063 study, a phase 2, single-arm, open-label study of nivolumab administered as a single agent in patients with advanced NSCLC who have progressed after at least two prior systemic treatments, were announced last month in conjunction with the 2014 Chicago Multidisciplinary Symposium on Thoracic Oncology. After approximately 11 months of minimum follow up, the estimated one-year survival rate was 41 percent and median overall survival was 8.2 months, reported nivolumab’s maker Bristol-Myers Squibb.

	Keytruda is also being tested against many tumor types, including NSCLC. Indeed, it is already approved to treat melanoma. Like nivolumab, Keytruda shows activity against both squamous and non-squamous NSCLC. Overall response rates to the compound are just under 20 percent, but they are higher in some subgroups, and those who respond tend to have durable responses.

	Progression-free survival in one trial topped 80 weeks for 40 percent of the patients whose tumors appeared likely to respond.

	“The durability of the response is one of the most promising aspects of this type of immunotherapy,” Herbst said. “When it works at all, it tends to work for a long time because the immune system, unlike targeted therapies, is designed to attack any foreign cell rather than a single thing that may disappear as tumors mutate.”

	Herbst argued during his presentation that official trial results may be understating the benefits of immunotherapies by using the wrong endpoints to evaluate their efficacy. Treatments that work on substantially less than half of all trial patients rarely produce huge improvements in median results, yet those same treatments can greatly increase long-term survival rates, if they provide the durable benefits that immunotherapy seems to offer. Trials may also be understating patient response rates, Herbst said, by failing to detect some instances of “pseudoprogression,” when tumors grow larger even though treatment is working.

	Successful immunotherapy can initially induce tumor growth in many patients, Herbst said, perhaps because of an inflow of lymphocytes. The new therapies that Herbst discussed all work by making it harder for tumors to escape attack from the T cells of the body’s immune system. T cells are covered with receptors that help them distinguish normal cells from foreign invaders. Among the most important of those is programmed death receptor 1 (PD-1).

	When T cells encounter normal human cells, PD-1 binds with the programmed death receptor-ligand 1 (PD-L1) that’s expressed on the outside of healthy human cells, and that connection signals the T cell to stand down. When T cells encounter cells that lack PD-L1, they attack those cells and call other T cells for backup. Two agents currently in development targeting PD-L1 are MPDL3280A and MEDI4736.

	Many tumors evade that immune response, Herbst said, because they express PD-L1 and thus convince T cells to accept them as normal. Both nivolumab and Keytruda are anti-PD-1 antibodies that encourage the immune system to attack cancer by preventing receptors on T cells from binding with ligands on tumors.

	Other experimental treatments work on tumors rather than T cells, blocking the ligands to prevent them from binding with the PD-1 on the T cells. This slightly different approach to producing immune system response has also produced very encouraging results in early-stage trials against lung cancer and other tumor types.

	Trials of both types of compound show that patients whose tumors express large amounts of PD-L1 are several times more likely to respond than those whose tumors appear to express little or none of ligand on their surfaces. Their responses also tend to last longer. That said, predicting who will benefit from which treatment remains an inexact science. Fewer than half of all people with high ligand expression respond to any given treatment, and as many as 10 percent of all people with little detectable ligand expression respond to some treatment.

	Herbst briefly discussed ongoing efforts at his lab and others to develop additional screening methods that will predict response with even greater accuracy. “The good news is that, although there is certainly some benefit overlap, the trials suggest that different treatments probably benefit different patient populations with different tumor biologies,” said Herbst, who is particularly happy to report that immunotherapy benefits many people with tumors that will not respond to any therapy targeted at particular genetic mutations.

Andrew D. Smith

As Large As Life: Using Artificial Intelligence in Cancer Care

Is Immunotherapy Ready for Prime Time in Breast Cancer?

Immunotherapies Continue to Impress in Lung Cancer Trials

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