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Educational theme: Monitoring treatment response in lymphoma using cfDNA

Feb 16, 2021
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This article is part of an educational series exploring the role of circulating tumor DNA in the management of patients with lymphoma.

Therapeutic efficacy monitoring and identification of tumor-specific mutations are invaluable for lymphoma treatment. However, tumor tissue biopsies are invasive and only provide a limited picture of the immune response, while peripheral blood samples only depict the circulating pathological compartment. Use of a surrogate marker, such as cell-free DNA (cfDNA) obtained by liquid biopsy, offers the potential for a more convenient and less invasive method for tumor assessment and monitoring. However, the development of techniques to extract cfDNA and subsequently measure treatment response is challenging and it is crucial to ensure their accuracy and specificity before use in routine clinical practice.

During the 62nd American Society of Hematology (ASH) Annual Meeting and Exposition, two abstracts were presented on recent advances in these techniques. These were previewed in our pre-ASH summary article here and are covered in more detail below.

T-cell receptor (TCR) diversity and dynamics1

Understanding changes in TCRs and how the repertoire of T cells contributes to the immune response is important in tumor-targeted therapies for lymphoma. Information about both circulating and tissue-derived DNA can be obtained from cfDNA, however current methods are suboptimal due to the fragmented nature of cfDNA. Navika Shuka and colleagues developed Sequence Affinity capture & analysis By Enumeration of cell-free Receptors (SABER), an optimized cfDNA-based TCR profiling method.

To validate SABER, blood samples were taken from 75 patients with lymphoma and 18 healthy controls, and sensitivity of T-cell clone identification was compared to other currently available methods. Compared to existing methods, SABER was more sensitive in the detection of T cell clonotypes from cfDNA:

  • vs amplicon-based sequencing with MiXCR analysis, p = 0.017
  • vs hybrid capture sequencing with MiXCR analysis, p = 8.122 × 10-5

Moreover, SABER was able to identify the malignant clonal TCR rearrangement in 7/9 patients with mature T-cell lymphoma.

In patients undergoing chimeric antigen receptor (CAR) T-cell therapy for diffuse large B-cell lymphoma, monitoring of TCR repertoire with SABER showed that the TCR repertoire size correlates to CAR19 T-cell levels in peripheral blood as measured by flow cytometry (r = 0.38; p = 0.008), but even more strongly with CAR19 levels in cfDNA (r = 0.68; p = 2.20 × 10-7). Differences in the dynamics of CAR19 T-cells and the total T-cell repertoire were found, with CAR T-cell activity peaking 7 days after infusion, whereas total T-cell activity was delayed and peaked 28 days after infusion. Significantly larger T-cell repertoire expansion but similar levels of CAR19 T-cell activity were observed in patients who responded to CAR T-cell therapy compared with those who did not (p = 0.0079). By contrast, absolute lymphocyte count and CAR T-cell activity were similar between responders and non-responders. Furthermore, in these patients with B-cell lymphoma, the T-cell repertoire became more clonal with time following post-lymphodepletion:

  • Baseline post-lymphodepletion vs 7 days post-lymphodepletion, p = 0.018
  • Baseline post-lymphodepletion vs 28 days post-lymphodepletion, p = 3.6 × 10-5

In addition, increased TCR repertoire expansion (p = 0.001) and increased clonality (p = 0.021) at 1-month post-CAR19 infusion were significantly associated with longer progression-free survival. Lastly, the authors showed with Cox proportional hazard modelling that the total T-cell repertoire activity and not CAR19 cell activity alone is a better progression-free survival predictor.

Feasibility of using cfDNA to monitor CAR T-cell therapy2

In a study by Aaron Goodman and colleagues, a median of ten plasma samples were collected from 12 patients with refractory diffuse large B-cell lymphoma during conditioning chemotherapy prior to CAR T-cell therapy. An automated, bead-based method was used to extract cfDNA before genome-wide sequencing analysis. For copy number alteration quantification, the genomic instability number (GIN) was used. GIN was calculated as the absolute deviation of observed normalized sequencing read coverage from expected normalized read coverage summed across 50,034 autosomal segments. Patients with copy number changes consistent with the presence of a tumor were identified using a GIN threshold value of 170, as previously described.3

Of the 12 patients in the study, eight were male, and the median age was 52 years (range, 38–77). Eleven patients received axicabtagene ciloleucel and one received tisagenlecleucel. Responses to CAR T-cell therapy were determined by positron emission tomography/computed tomography using the Lugano criteria and are shown in Table 1.

Table 1. Response to chimeric antigen receptor T-cell therapy in patients with refractory diffuse large B-cell lymphoma2

 

N = 12

Median duration of follow-up, days (range)

246.5 (34–389)

Best response, n (%)

 

Complete response

7 (58)

Partial response

2 (17)

Stable disease

1 (8)

Progressive disease

2 (17)

Disease status at last follow-up, n (%)

 

Complete response

4 (33)

Progressive/relapsed disease

8 (67)

Elevated GIN (> 170) was detected in ten patients at baseline, and in all 12 patients at follow-up. In patients with a complete response (n = 5), GIN decreased to < 170 by 50 days and remained below the threshold thereafter. By contrast, GIN did not decrease below the 170 threshold in patients who initially responded but then relapsed (n = 2). Of the five patients who partially responded, or who developed stable or progressive disease, four showed an initial decrease in GIN with conditioning chemotherapy treatment and CAR T-cell therapy, which then increased above 170, mirroring clinical response.

Conclusion

These studies confirm the potential of cfDNA as a minimally invasive surrogate to monitor therapeutic efficacy in patients with lymphoma. The development of technologies like SABER is promising and will enable more detailed analysis of the immune response following treatment. Quantification of cfDNA using GIN will enable clinically meaningful information to be obtained from liquid biopsies and may also have the ability to predict clinical response to therapy.

  1. Shukla ND, Craig AFM, Sworder B, et al. Profiling T-cell receptor diversity and dynamics during lymphoma immunotherapy using cell-free DNA (cfDNA). 2020;136(Supplement 1):49-50. DOI: 10.1182/blood-2020-141655
  2. Goodman AM, Holden KA, Jeong A-R, et al. Response to CAR-T therapy can be monitored using genome-wide sequencing of cell-free DNA in patients with DLBCL. Blood. 2020;136(Supplement 1):17. DOI: 1182/blood-2020-141010
  3. Jensen TJ, Goodman AM, Kato S, et al. Genome-wide sequencing of cell-free DNA identifies copy-number alterations that can be used for monitoring response to immunotherapy in cancer patients. Mol Cancer Ther. 2019;18(2):448-458. DOI: 1158/1535-7163.MCT-18-0535

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