Analysis of the immune microenvironment in pre-treatment non-small cell lung cancer (NSCLC) patients with follow-up response data to second line immunotherapy


M Bhagat(1), N Elliott(2), S Warren(2), C Womack(1), L Memeo(1), L Colarossi(1), M Cumberbatch(1)
(1)TriStar Technology Group, LLC, Washington, DC 20006, USA, (2)NanoString Technologies, Inc, Seattle, WA 98109, USA.





background

Patients selected based on PD-L1 expression for second line immune checkpoint inhibitor (ICI) treatment are often identified using archival specimens collected months or years prior to starting immunotherapy. Such patients may have received and failed multiple lines of standard of care (SOC) treatments with subsequent potential impact on PD-L1 expression and immune microenvironment.

methods

We have analysed formalin fixed paraffin embedded (FFPE) tissues in a cohort of NSCLC patients taken during resection performed as first line surgical treatment for which radiotherapy, SOC chemotherapy, and second line immunotherapy clinical follow-up data are available. The immune microenvironment was evaluated by immunohistochemistry (IHC; n=18 patients) plus digital image analysis (CellProfiler™) for CD3 (2GV6) and CD8 (SP57), PD-L1 (22C3) was scored by a pathologist for tumour proportion score (TPS) and combined positivity score (CPS). Samples (n=22 patients) were analysed by NanoString using the IO360TM gene expression panel. The aim of the study was to explore whether immune signatures predictive of response to ICI therapy may be identified in such samples.


results

Clinical follow-up data indicated objective response to ICI therapy for 4 patients, with time from first diagnosis to receiving second line ICI treatment ranging from 5 to 102 (mean 33.6) months (Table 1). During this time these patients failed various lines of radiotherapy and SOC chemotherapy prior to receiving immunotherapy.

wdt_IDGenderAge (years)Stage at diagnosisSub-TypeImmunotherapyTime from first diagnosis to immunotherapy (months)Overall survival (OS) (in months)Progression free survival (PFS) (in months)Alive/DeadResponder status
1M63IIIBNSCLCNivo566AR
2M63IIIAADCDurva1677AR
3M57IBADCPembro88No data4No dataR
4M71IIAADCNivo1021717AR
5M78IIBSCCNivo3555DNR
6-62IIIASCCNivo2522DNR
7M59IIIAADCNivo1200DNR
8F57IIIASCCPembro622ANR
9M59IIIAmicropapillary ADCNivo1200DNR
10M70IAadenosquamous carcinomaNivo1377DNR


While the Tumour Inflammation Signature (Ayers et al., JCI 2017) was not predictive of response (not shown), gene expression analysis did identify several signatures associated significantly with response, including increased abundance of CD8 T cells, cytotoxic cells, cytotoxicity and MHC class II antigen presentation (Figure 1). PD-L1, whether measured by NanoString (Figure 1) or IHC (Figure 2), was found not to be significantly associated with response to immunotherapy in this small cohort of samples. The frequency of PD-L1 positivity by IHC was 5/18 ≥ 1% for TPS and 9/18 ≥ 1% for CPS. In support of the significant NanoString CD8/cytotoxicity signatures, CD8 T cells by IHC were elevated significantly in the responder versus non-responder populations (Figure 3).


figure 1

CD8 T Cell and Cytotoxicity NanoString Signatures Associated with Response




Figure 1. NanoString IO360 analysis of response. A) Forest plot of scores vs. response showing the association of signatures with response (R: responder; NR: non-responder). Points represent mean log2 fold-changes for signatures between R and NR; lines show 95% confidence intervals; larger boxes indicate statistical significance, B) Boxplot of scores vs. response, NR; R, and associated ROC curves illustrating the predictive performance of a signature score; predictive signatures have a curve that reaches the top left corner; shading shows 95% confidence intervals.

(Disclaimer: For Research Use Only. Not for use in diagnostic procedures.)

figure 2

No Association of PD-L1 Expression (TPS or CPS) with Response


Non Responder

Immunotherapy: Nivo
% PD-L1 TPS: 100
% PD-L1 CPS: 100
CD8/mm2: 928

Responder

Immunotherapy: Durva
% PD-L1 TPS: 20
% PD-L1 CPS: 23
CD8/mm2: 1528
Figure 2. PD-L1 expression. Samples were stained by IHC for PD-L1 (22C3) and A) scored by a pathologist for TPS and CPS, B) representative images of positive PD-L1 expression.


figure 3

Tumour Infiltrating Lymphocytes are Significantly Associated with Response


Figure 3. Analysis of tumour infiltrating lymphocytes by IHC. Samples were stained by IHC for CD3 and CD8. Digital images were acquired using an Aperio scanner and analysed using CellProfilerTM to deliver the number of immune cells/mm2. Data are expressed as mean cells/mm2 for NR versus R populations (A; *p<0.05), or plotted as individual data (B). Mean data values (±SE) for CD3, CD8 and PD-L1 are summarised (C). Representative images for CD3 and CD8 for NR and R cases are shown (D)
wdt_IDStatusCD3CD8TPS (PD-L1)CPS (PD-L1)
1R1743 ± 410961 ± 2615 ± 56 ± 6
2NR743 ± 105329 ± 578 ± 79 ± 7
Figure 2. PD-L1 expression. Samples were stained by IHC for PD-L1 (22C3) and A) scored by a pathologist for TPS and CPS, B) representative images of positive PD-L1 expression.


conclusions

  • These data demonstrate that PD-L1 expression between two sites in the same tumour, and between primary versus synchronous metastases is remarkably consistent.
  • Frequencies of non-concordance are remarkable similar whether 25% or 1% are used to define positivity.
  • The highest prevalence of tumour membrane PD-L1 positivity was observed for lung cancer and H&N cancer, with colorectal adenocarcinoma showing the lowest level of PD-L1 staining.
  • The conclusions drawn are that a representative PD-L1 score may be achieved from a single FFPE tumour block, and that for-cases where obtaining tissue from a primary tumour may be challenging, PD-L1 could be evaluated in metastatic tissue.
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