Publications

Mass cytometry reveals classical monocytes, NK cells, and ICOS+ CD4+ T cells associated with pembrolizumab efficacy in patients with lung cancer

Rochigneux et al. Clin Cancer Res (2022).

Immune profiling of NSCLC patient blood prior to treatment provides a less invasive option for predicting response to anti-PD-1 therapy

A biomarker is a biological molecule found in blood or tissues that may be used to see how well the body responds to a treatment. For patients with non-small cell lung cancer (NSCLC), only a single biomarker exists that can help predict whether immunotherapy treatment would be successful: PD-L1 expression on cancer cells. Rochigneux and colleagues set out to identify new candidate immune cell biomarkers associated with successful clinical outcomes. Using a 30-marker mass cytometry panel, the team profiled baseline blood samples from 27 patients with advanced NSCLC who were enrolled at UCLA in the KEYNOTE-001 study of anti–PD-1 immune checkpoint inhibitor (pembrolizumab) monotherapy.

The authors identified 15 main myeloid and lymphoid cell populations associated with better survival; however, none of these independent associations was significant. To test the combined predictive potential, the authors created a 0-3 point scoring system using the baseline frequencies of the three immune cell subsets that were most associated with survival: classical monocytes, NK cells, and T-cells expressing ICOS and CD4. When the frequency of one or more of these subsets passed a threshold the score increased, with higher scores associated with better survival.

Rochigneux and colleagues found that a baseline immune peripheral score of 0 or 1 identified patients with progressive disease, and scores of 2 or 3 marked patients with either stable disease or partial response and was strongly associated with better survival. This scoring system was further validated using a publicly available mass cytometry dataset from 15 patients with metastatic melanoma treated with nivolumab or pembrolizumab.

In all, this manuscript highlights the utility of mass cytometry to comprehensively profile peripheral blood samples—partitioning 15 immune cell groups using 30 cell-surface markers simultaneously—and identify biomarker signatures of response to immune checkpoint inhibitors in NSCLC that could be easily assessed in the clinic.

Reference citation: Rochigneux P, Lisberg A, Garcia A, Granjeaud S, Madroszyk A, Fattori S, Gonçalves A, Devillier R, Maby P, Salem N, Gorvel L, Chanez B, Gukasyan J, Carroll J, Goldman J, Chretien AS, Olive D, Garon EB. Immune profiling of NSCLC patient blood prior to treatment provides a less invasive option for predicting response to anti-PD-1 therapy. (2022). Clin Cancer Res 28:5136–48. DOI: 10.1158/1078-0432.CCR-22-1386

Illustration. In the center is a grey mass labeled "tumor" under a magnifying glass. Inside the tumor are three grey cells all with "zzz" above them. Outside the magnifying glass (and therefore outside the tumor) in the four corners are different depictions. Top left shows three red cells and is labeled "novel T cell clones". Top right shows a lymph node with red T cell zones and the words "LN trafficking required". Bottom right shows two red T cells in a blood vessel labeled "LAG-3 and CD8+" with the words "peripheral biomarkers". Bottom left shows a spleen with the same grey, sleeping T cells. There's a red lightning bolt and the words "impaired systemic immune response in cancer".

Peripheral blood analysis is key to developing effective cancer immunotherapies

Although most research focuses on tumor-killing immune cells at the site of the tumor, new research increasingly demonstrates the value of studying circulating immune cells in peripheral blood to identify new therapeutic targets to enhance tumor-killing immune responses. We highlight recent publications with peripheral blood biomarkers and discuss recommendations for how to incorporate peripheral blood immune profiling into your drug development pipeline.

The complementary capabilities of single-cell RNA seq and high-dimensional cytometry

Researchers have used scRNAseq and CyTOF as complementary tools in a wide variety of published studies to provide additional breadth, depth, and coverage when analyzing cellular processes. Here are a few specific examples that illustrate the diversity and range of these approaches in a variety of biomedical research settings.

A ring of grey and black in the center split into 3 sections with the words Flow, Mass, Spectral going around it clockwise. A dashed line coming out from the ring separates the rest of the space into three sections. Surrounding the ring are six colored circles each with a white icon. The circles directly opposite each other are the same color, but different shades (red opposite pink, blue opposite light blue, green opposite light green). Red shows a pencil, pink shows a grid-like matrix, Blue shows four dials, light blue shows only two; green shows a heavy metal spectrum with distinct lines, light green shows fluorescence spectrum with overlap. The green circle is in the Mass segment, the red overlaps between Mass and Flow, the Blue overlaps between Mass and Spectral. The Mass segment is the only one without any light colored circles.

Flow vs. Spectral vs. Mass Cytometry: What’s the difference?

What’s the difference between flow, spectral, and mass cytometry? We highlight the differences between these three different cytometry platforms used by drug developers to characterize the immune system so you can determine which platform provides you the richest information to advance your research and therapeutic development.

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Peripheral blood analysis is key to developing effective cancer immunotherapies

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