Inhye E. Ahn, MD, Discusses the Use of Ibrutinib Therapy in CLL

Dr Ahn, Hematologist/Oncologist and Staff Clinician, Hematology Branch, National, Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, discusses a 4-factor prognostic model to help predict the outcomes of ibrutinib therapy over chemoimmunotherapy in patients with chronic lymphocytic leukemia (CLL).

Transcript

My name is Inhye Ahn. I'm hematologist/oncologist and staff clinician at the National Heart, Lung, and Blood Institute at the National Institutes of Health. My area of specialty is chronic lymphocytic leukemia.

Ibrutinib is the first-in-class BTK inhibitor approved by the FDA for the treatment of CLL, both in the frontline setting as well as in the relapsed or refractory disease study. Randomized trials established the superiority of ibrutinib-based therapy over chemoimmunotherapy in CLL. Although ibrutinib is highly efficacious in CLL, single-agent ibrutinib is not curative, and drug resistance can emerge. 

The clinical course of ibrutinib-resistant CLL that emerges can be aggressive, and the durability of response to alternative therapy is limited. We came from this question that the variability in clinical outcome which led us to develop a risk stratification tool specific to the context of the ibrutinib therapy. 

We collaborated with Pharmacyclics, the manufacturer of ibrutinib, and combined data from 6 clinical trials, testing ibrutinib for the treatment of CLL. The combined cohort comprised 804 CLL patients treated with ibrutinib or ibrutinib-based therapy. 

The vast majority of these patients, more than 80 percent, in fact, received single-agent ibrutinib. About 16 percent received ibrutinib with obinutuzumab. More than half of these patients had relapsed CLL, and about 50 percent received ibrutinib as first-line therapy. 

We applied traditional multivariate analysis to train and validate a prognostic model. In parallel, we use machine-learning algorithms to identify prognostic factors relevant to progression-free and overall survival. 

With that, we found that there are 4 factors associated with both progression-free and overall survival, which were TP53 aberration, prior treatment status, high beta-2 microglobulin, and high lactate dehydrogenase, or LDH in short. These factors were independently associated with inferior progression-free and overall survival in patients treated with ibrutinib. 

We used these 4 factors to build a 4-factor scoring system, which stratified patients into high-, intermediate-, and low-risk groups based on the number of factors present at baseline. 

What we found is that at 4 years, more than half of the patients in the high-risk group had progressed or died in contrast to about 10 percent of the patients in the low-risk group. There was a vast difference in their outcome. 

Surprisingly, this clinical model showed strong correlation with the genomic biomarkers of drug resistance. What I mean by that is that there are mutations called BTK and PLCG2, which are found in about 80 percent of the patients who progressed with ibrutinib-resistant CLL. These mutations were found most frequently in the high-risk group and least frequently in the low-risk group. 

Another important mechanism of ibrutinib resistance is Richter's transformation, which does not accompany these mutations, but have a rare and aggressive histologic transformation of CLL to an aggressive lymphoma. We found that a high-risk group had the most frequent development of Richter's transformation, and none of the patients in the low-risk group in contrast had Richter's transformation. 

What the 4-factor model uses are the really simple and commonly used factors that the clinicians use day to day for CLL patients with prognostications. This four-factor model can inform clinical decision-making and patient counseling. 

In practice, for instance, the scoring system can help set expectations, guide the frequency of monitoring, and direct patients to risk-adapted treatment approaches on the basis of predictive risk of early progression. 

In research, the model provides a platform for the investigation of risk-adapted treatment trial designs.

There are lots of questions that this project has brought us. One of them is “What the role of this model is?” The vast majority of the patients validated in this model were relapsed/refractory CLL patients.

Separately, an independent group of investigators in Italy validated the model in a cohort of more than 500 patients. The model works very well which is great news, but at the same time, that cohort mostly were relapsed/refractory CLL. 

My question is whether this model can be useful for patients who are getting ibrutinib as their first-line therapy. My next step is to investigate that as well as different targeted agent therapy, such as venetoclax. 

I want to highlight the limitations of this project. It is possible that the 4-factor model could be improved by the inclusion of additional parameters, such as complex karyotype. The complex karyotype and the dataset that we analyzed were not available where more than half of the patients precluding the analysis, so it was excluded. 

Nevertheless, other studies have revealed that complex karyotype may be an important prognostic factor in CLL in the context of ibrutinib therapy.