Study of quadruple combination for induction resistant myeloma reveals novel immune checkpoints

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Published: 12 Dec 2019
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Dr Yael Cohen - Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

Dr Yael Cohen speaks to ecancer at the ASH 2019 meeting in Orlando about results from the KyDaR trial,

She explains that the multi-centre trial uses the quadruple combination of carfilzomib, lenalidomide, dexamethasone and daratumumab (KRD-D) for induction resistant myeloma, along with conducting translational single-cell analysis.

Dr Cohen reports that not only did the patients show deep and sustained responses to the combination, but the trial also identified potential drivers of advanced resistance, including novel immune checkpoints.

ecancer's filming has been kindly supported by Amgen through the ecancer Global Foundation. ecancer is editorially independent and there is no influence over content.

ecancer's filming has been kindly supported by Amgen through the ecancer Global Foundation. ecancer is editorially independent and there is no influence over content.

Study of quadruple combination for induction resistant myeloma reveals novel immune checkpoints

Dr Yael Cohen - Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

In this abstract I’m providing data on the KyDaR trial. This is a clinical trial that looked at a quadruple combination of carfilzomib, lenalidomide, dexamethasone together with daratumumab, so KRD-Dara, in a population of patients who are induction failure. Induction failure means that the patient failed on their first treatment regimen when they were newly diagnosed myeloma, bortezomib based regimen, and they either did not reach a partial response in four cycles or progressed on therapy or they had an early relapse, that is within 18 months from the initiation of the study. So this is known to be a very poor outcome population with a compromised survival. Therefore they are with a need for a better therapy and we gave them KRD-Dara and looked at their clinical outcomes but another very important aspect is the translational aspect. We did single cell RNA sequencing for all these patients when they entered the study and then at 3 months, 9 months and for those who progressed upon progression.

We were able to show that this combination was safe and tolerable, similar to Dara-RD and KRD in this relapsed population. We had a higher response rate, the overall response rate was 85%, and many of the patients had deep and sustained responses. When we looked at the single cell RNA sequencing, this is a platform that was developed in the Amit lab at the Weizmann Institute and basically it puts together the power of sorting and identifying myeloma cells together with MARS-seq amplification of the RNA. So the RNA of each myeloma cell is amplified on a single cell basis and we are able to get a clean picture of the transcriptional map for each subclone in each myeloma patient. So this really allows us to identify what are the changes that the patient’s myeloma cells undergo when they are exposed to therapy. We were able to look at and identify patterns of drivers that were upregulated in relapsed patients and also identify potential new druggable targets that might give new hope for these patients.

We were actually able to identify some genes, some new genes, that were not previously associated with myeloma progression. There is further work to try and see what the significance is and whether these might be potential targets for patients. We also were able to show at the subclonal level to identify what’s happening. So we can look at the picture in the screening when the patients go into the study and then along the study we can see actually the clonal evolution as it develops. So some clones go down, some other clones come up and we can actually see at the gene expression level what are the changes that are happening to these patients. So it gives us a whole new look on what is happening under the surface.

We were also able to develop a prediction model. So we were able to look at the transcriptome of the patients at screening and using a machine learning technology, artificial intelligence, we were able to build, based on a neural network, a model that can predict which of the patients is going to respond to this treatment. So this is really a proof of concept showing that perhaps we would be able to in the future tailor patient treatment according to their transcriptional patterns instead of just using various heuristics as we do at this time. So this might allow us to optimise the responses and to be able to choose wisely between treatments that are available.

The study is actually still ongoing and we have a vast amount of data and we’ve just really started to look at it. It will be interesting to look at these potential targets and to try and make sense of them and also to further look at this prediction model and to see how it tells out. We would want to also validate it on further patient populations to see how it holds up and then perhaps also do similar analysis for other treatment interventions.