Therapy for patients with multiple myeloma encompasses many agents and potential combinations, but truly personalizing care will require knowing how those agents interact for maximum efficacy and incorporating patient preferences for toxicity into the care plan.
Daniel Auclair, PhD
Therapy for patients with multiple myeloma (MM) encompasses many agents and potential combinations, but truly personalizing care will require knowing how those agents interact for maximum efficacy and incorporating patient preferences for toxicity into the care plan, Daniel Auclair, PhD, explained at the 23rd Annual International Congress on Hematologic Malignancies®.1
By the same token, therapy should not boil down to cost or quality-of-life years (QALYs) gained. “Ultimately, it shouldn’t be about QALY; it should be about what’s the best treatment for a patient given his or her own unique disease,” said Auclair, chief scientific officer at the Multiple Myeloma Research Foundation (MMRF).
Auclair discussed his involvement in CoMMpass (NCT01454297), a prospective study of 1154 patients from 90 sites worldwide that is tracking patients with MM to gain improved understanding of responses to a spectrum of therapies and therapy sequences (FIGURE). In CoMMpass, patients with untreated MM receive immunomodulatory or proteasome therapy or a combination of both and, upon relapse, physician’s choice or clinical trial enrollment.
Clinical assessments are conducted every 3 months for 5 years and then every 6 months for 3 more years. Data collected include adverse events, quality of life and patient-reported outcomes, residual disease, flow cytometry, and molecular and immune profiles. “Every time there is a change in the patient’s condition, we redo biopsies, these aspirates, and keep doing some science,” Auclair said.
The trial has already tracked patients on a multitude of agents, yielding a trove of data on combinations and outcomes as patients accrue and the trial continues. “The beauty of a trial like this is [that] you can keep learning a lot about these newer agents from a clinical perspective, as well as a quality science perspective,” Auclair said.
Often in a small trial, little can be discerned about how different drugs work in patients with diverse characteristics. “You’re dealing with the tyranny of small numbers,” Auclair said. He cited an initial cohort of 41 CoMMpass patients who received frontline VRd (bortezomib [Velcade], lenalidomide [Revlimid], and dexamethasone) and stem cell transplantation and on progression were treated with carfilzomib (Kyprolis)— or daratumumab (Darzalex)–based regimens or 1 of multiple others.
With much larger enrollment, there is potential for the data to reveal much more substantive insight. “Imagine that now you have a database where instead of having 40 individuals, you have 400 or 4000, and you could start studying deeply how the choices from 1 line to the next changed the outcome for these patients, and you could learn some of the science of why this is happening,” Auclair explained.
CoMMpass provides that opportunity, now that it has employed many agents. The most common drugs in the study include dexamethasone, used by close to 100% of patients (n = 1105), followed by lenalidomide (n = 921), bortezomib (n = 865), cyclophosphamide (n = 565), melphalan (n = 527), carfilzomib (n = 417), pomalidomide (Pomalyst; n = 158), daratumumab (n = 139), and prednisone (n = 91), followed by more than 50 other agents with lesser degrees of usage. Many patients benefit from current therapies in MM, but 20% relapse or die within 2 years, according to Auclair. CoMMpass findings may improve upon that statistic. Using whole genome sequencing, investigators analyzed the translocation landscape of 826 patients with newly diagnosed MM.
In 10% of patients, translocations at the immunoglobulin lambda light chain (IgL) locus were present and corresponded with poor prognosis. Importantly, 70% of those patients turned out to be hyperdiploid, a marker of standard risk. “Here’s a chromosomal abnormality which probably is not a driver of high-risk disease itself but actually what it does is confer insensitivity to IMiDs [immunomodulatory drugs]. In essence, you have these hyperdipliod patients you would maybe treat in a normal way, maybe not in an aggressive way, but because they have this interesting alteration, [they] won’t respond to IMiDs,” Auclair said.
CoMMpass investigators concluded that “the majority of IgL-translocated myeloma is being misclassified,” because the IgL translocation is among the strongest oncogenic drivers in MM.2
As useful as knowing the IgL translocation is, based on this finding, it can be spotted only through next-generation sequencing, which requires that physicians reach beyond the standard battery of tests. “This really brings us back to this question about how fast we should bring these next-generation technologies to the forefront, even in the real world,” Auclair said. This finding also highlights how valuable large trials and highly focused qualitative studies can be in excavating data to find patterns of relevance, he added.
Another study, the MMRF CureCloud Research Initiative (NCT03657251), is designed to accomplish both objectives via large-scale accrual and qualitative analysis by consolidating patient records, employing a mobile phlebotomist to take samples, and compiling immune and genomic analyses. The target is to enroll 5000 patients, Auclair said.
In a preliminary study, a patient preference survey was issued to 94 relapsed patients to catalog how they feel about attributes and potential adverse events associated with next-line treatment options.3 Two subgroups were identified with distinctly different preferences and concerns regarding treatment toxicity versus progression-free survival (PFS), although both expressed willingness to trade PFS for less toxicity. Patients were also surveyed about potential exposure to nerve damage, gastrointestinal problems, risk of low blood counts and heart failure, and mode of drug administration, whether oral or intravenous.
“This is a pilot that we’re expanding to a lot of patients,” Auclair said. “We’re trying to figure out if we can say more to providers about different types of patients: ‘That type of patient may not care about adverse events and may discontinue treatments versus this type of patient’ or ‘Patient B may go through everything in a very resilient way to get the maximum benefit from his treatment.’” This way, patient preference can be factored into the optimal sequencing of therapies. “There’s a role for everyone to play in this, including the patient,” Auclair said. “Let’s finally make them active participants in the research rather than simply human subjects.”