各位好！今日与大家分享一篇发表在预印本上的SAKK16/14 trial的转化研究部分。在ASCO会议的一堆热点研究的轰炸下，一起来看看新辅助化疗序贯免疫治疗的转化研究会为我们指出哪些不错的研究方向？此文又有哪些值得学习的地方？
Tumor immune dynamics and long-term clinical outcome of stage IIIA NSCLC patients treated with neoadjuvant chemoimmunotherapy
Dominic Schmid1,2,*, Bettina Sobottka3,*, Massimiliano Manzo4,*, Marta Trüb1,2,*, Katharina Leonards4, Petra Herzig1,2, Philip Jermann4, Stefanie Hayoz5, Spasenija Savic Prince4, Giulia Tochtermann3, Marina Natoli1,2, Miklos Pless5, Adrienne Bettini6, Martin Früh7, Laetitia A. Mauti8, Christian Britschgi8,9, Solange Peters10, Michael Mark11, Adrian F. Ochsenbein12, Wolf-Dieter Janthur13, Christine Waibel14, Nicolas Mach15, Patrizia Froesch16, Martin Buess17, Pierre Bohanes18, Michel Gonzalez19, Ilaria Alborelli4,&, Sacha I. Rothschild1,2,14,&, Viktor Hendrik Koelzer3,4,&, Alfred Zippelius1,2,&
This preprint is Under Review at Nature Portfolio. 18 Apr, 2024
Neoadjuvant chemoimmunotherapy offers promise to improve outcomes for patients with resectable non-small cell lung cancer (NSCLC). Yet, not all patients derive treatment benefits and reliable biomarkers of response are still lacking. We here assess the long-term clinical outcome of neoadjuvant chemotherapy and perioperative anti-PD-L1 inhibition in resectable stage IIIA NSCLC in the SAKK 16/14 trial and provide a comprehensive characterization of anti-tumor immune responses for biomarker-based treatment personalization. At a median follow-up of 5.3 years, the median event-free survival (EFS) was 4.0 years while median overall survival was not reached. Computer-aided spatial image analysis emphasized the importance of CD8+ T cell positioning in tumors, and larger tertiary lymphoid structures in pre-treatment biopsies correlated with improved EFS. Genomic techniques revealed the association of intratumoral TCR diversity with response. Finally, circulating proliferating CD39 ⁺ PD-1 ⁺ CD8 ⁺ T cells and elevated levels of CCL15 post-treatment were seen in patients with sustained therapeutic benefit.新辅助化疗免疫治疗有望改善可切除非小细胞肺癌(NSCLC)患者的预后。然而，并不是所有的患者都能获得治疗益处，而且仍然缺乏可靠的反应生物标记物。在SAKK 16/14试验中，我们评估了新辅助化疗和围手术期抗PD-L1抑制在可切除阶段IIIA NSCLC中的长期临床结果，并为基于生物标记物的治疗个性化提供了抗肿瘤免疫反应的综合表征。中位随访期为5.3年，中位无事件生存期(EFS)为4.0年，但未达到中位总生存期。数字病理空间图像分析结果强调了CD8+T细胞在抗肿瘤反应中的重要性，治疗前活检中较大的三级淋巴结构与改善的EFS相关。基因组技术揭示了肿瘤内TCR多样性与疗效的关系。最后，在具有持续治疗疗效的患者中，治疗后循环中增殖的CD39、⁺、PD-1、⁺、CD8、⁺T细胞和CCL15水平升高。
1. SAKK16/14作为较早开展新辅助化疗序贯免疫治疗的II期多中心研究，2016年开始入组，研究的主要结果发表在JCO上，既往公众号内也对前期结果进行了总结。点击图片回顾前情。
2.详看细节：①SAKK16/14在临床和转化研究设计上都具有领先性，临床点上只纳入N2患者，序贯化疗、免疫，辅助免疫维持1年。虽然目前新辅助领域以化免联合为主流，但这一序贯设计和辅助治疗维持方案仍然具有可圈可点之处。
②转化研究设计上基线、治疗后切除标本以及4个关键节点的血标本留存：基线、化疗后、免疫结束手术前、辅助治疗3周期后。
③值得注意的是虽然研究表示基线穿刺标本的CD8+T cell、TLS（出现与否和大小）与1年EFS（你细品这个主要研究终点，选择的多么美~！）有关，但是14个配对样本的结论仍然需要更多的验证。当然文章似乎漏掉的一个结局相关性指标是Nodal downstaging，虽然边边角角的，但是值得我们思考为什么。
④血液、组织样本的多组学分析外加中位随访5年以上，请问你觉得这篇文章最终会被Nature旗下的哪个子刊接收呢？
⑤强烈推荐读下文章的讨论部分，把目前可能与短期疗效、长期预后探索的转化研究方向都聊了一遍。此文和NeoCoast研究的转化部分值得一起参看。
3. 如果给你一个位置，2021年欧洲某肿瘤中心主任，你该怎么设计及开展新辅助领域的转化研究呢？一起来看看charocampelo的答案吧。
目录
1. INTRODUCTION
2. Materials & Methods
2.1 Follow-up clinical data
2.2 Blood samples
2.3 Pathology samples
2.4 Mass cytometry (CyTOF) sample preparation and surface staining
2.5 CyTOF data analysis
2.6 Flow cytometry staining
2.7 scRNAseq dataset re-analysis
2.8 Nucleic acid extraction
2.9 TCR sequencing and data analysis
2.10 Serum cytokine analysis
2.11 Mutation analysis
2.12 Differential gene expression analysis
2.13 Digital pathology
2.14 Data availability
3. Results
3.1 Five-year clinical outcomes of the SAKK 16/14 trial
3.2 High CD8+ T cell densities in the tumor compartment are associated with improved EFS
3.3 Presence of large pre-treatment TLS in tumor tissue is associated with improved EFS
3.4 Increased intratumoral T cell diversity is associated with improved EFS
3.5 Remaining tumor cell content dictates differential gene expression of NSCLC tumors post-neoadjuvant chemoimmunotherapy
3.6 Post-treatment circulating Ki-67-expressing CD39+ PD-1+ CD8+ T cells are associated with improved EFS
3.7 Serum CCL15 correlates with sustained therapeutic benefit in a small subset of patients
4. Discussion
— 图表汇总—
3. Results
3.1 Five-year clinical outcomes of the SAKK 16/14 trial
Fig. 1. Five-year clinical outcomes of the SAKK 16/14 trial.
a, Flow chart of the SAKK 16/14 trial and biospecimen collection.
b, Kaplan-Meier curves of event-free survival (EFS) and c, overall survival (OS) at data cutoff on January 27, 2024. Median follow-up time was 5.3 years.
3.2 High CD8+ T cell densities in the tumor compartment are associated with improved EFS
Fig. 2. Spatial distribution of intratumoral immune populations.
a, Micrograph of three representative resection specimens classified as desert, excluded and inflamed tumors. Top row shows CD8 immunohistochemistry (IHC) image, bottom row shows mark-up with CD8+ T cells depicted in red and tumor cells in blue. Indents show scales.
b, Micrograph of representative tumor. Left, IHC image for cell annotation; middle, machine- learning-based image segmentation showing tumor compartment (TC) in red, stroma in blue and desmoplastic stroma in green; right, detection of cell populations in different tumor segments. Idents show scales.
c-d, Densities of cells expressing CD3, CD8, FoxP3, and CD20, respectively, in tumor and stromal compartments of initial biopsies (n = 21) acquired at TP1 (c) and resection specimens with residual tumor (n = 26), acquired at TP3 (d). Dashed line: highest mean CD3+ cell density. Dotted line: highest mean CD8+ T cell density detected. Immune excluded and inflamed tumors were distinguished by standard classification criteria22,23. Bars show means +SEM, bars and symbols depict both values for stroma (filled) and TC (empty).
e-i, Kaplan-Meier event-free survival (EFS) curves of patients in the SAKK 16/14 trial according to pre-treatment immune phenotype and immune cell densities in initial biopsies. Statistical significance was assessed with the log rank Mantel-Cox test.
3.3 Presence of large pre-treatment TLS in tumor tissue is associated with improved EFS
Fig. 3. TLS characterization via digital pathology.
a, Micrograph of representative tumor section illustrating detection of tertiary lymphoid structures. Left, CD20 immunohistochemistry (IHC) image; middle, machine-learning-based tissue segmentation; right, machine-learning-based identification of TLS and area quantification. Indents show scales.
b, Kaplan-Meier event-free surivival (EFS) curves of patients in the SAKK 16/14 trial according to average size of TLS in initial biopsies (n = 14). Statistical significance was assessed with the log rank Mantel-Cox test.
c, Analysis of CD8+ T cell density versus TLS size in initial biopsies (n = 14) with their TIME classified as excluded (n = 10) or inflamed (n = 4).
d, Modified swimmer plot showing EFS of patients with matched initial biopsies (TP1) and resections (TP3) (n = 13). Left column density of CD8+ T cells in the tumor compartment, right column shows average TLS size. Colors on the left and right denote the immune phenotype in biopsies (left) and resection (right). In patients with complete histological tumor regression, the data indicate the immune cell counts in the tumor bed (scar) area (shown in yellow).
3.4 Increased intratumoral T cell diversity is associated with improved EFS
Fig. 4. T cell receptor sequencing in post-treatment PBMCs and tumor tissue.
a-c, Differences in post-treatment (TP3) TCR repertoire metrics in tumor resection samples comparing patients achieving and not achieving a 12-months EFS (n = 34 vs. n = 15)
d-f, Differences in post-treatment (TP3) TCR repertoire in PBMCs patients achieving and not achieving a 12-months EFS (n = 36 vs. n = 16). Clonal richness (number of clones, a, d), evenness (b, e) and Hill-Simpson diversity (c, f) are shown. Statistical testing was performed with the Mann Whitney test.
Extended Data Fig. 1 Clonal space occupied by top 1% of clones depends on number of clones.
a, Clonal space occupied by the top 1% TCR clones of the total repertoire in tumor samples post-surgery (TP3), as analyzed in the NADIM trial53. Comparison by EFS ≥ 12 months (blue, n = 34) and < 12 months (red, n = 15). Statistical testing was performed with the Mann-Whitney test.
b, Number of clones versus clonal space occupied by top 1 % clones in patients with EFS ≥ 12 months (blue, n = 34) and in patients with EFS < 12 months (red, n = 15). Correlation was assessed with Spearman coefficient r and p value was calculated with two-tailed t test.
Extended Data Fig. 2 Tumor mutational burden and oncoprint.
a, b, Kaplan-Meier event-free survival (EFS) (a) and overall survival (OS) (b) curves of patients in the SAKK 16/14 trial according to tumor mutational burden (TMB, TMB < 10 mutations/Mb, n = 34 vs TMB > 10 mutations/Mb, n = 15).
c, TMB of patients achieving and not achieving a 12-months EFS (n = 12 vs. n = 37), d, major pathological reponse (MPR, (n = 20 vs. n = 29), and e, nodal clearance (n = 25 vs. n = 24).
f, OncoPrint showing the distribution of genomic alterations (rows) in individual participants (columns) of the SAKK 16/14 trial. The mutational frequency of each gene is labeled on the right. The relative TMB of each patient is shown on top (n = 47).
3.5 Remaining tumor cell content dictates differential gene expression of NSCLC tumors post-neoadjuvant chemoimmunotherapy
Extended Fig. 3 Differential gene expression of post-treatment tumor tissue.
a, Uniform Manifold Approximation and Projection (UMAP) of mass cytometry data of PBMCs post-neoadjuvant chemoimmunotherapy (TP3) in selected patients (n = 50’000 randomly selected cells). Responders comprise n = 8 pre-selected SAKK 16/14 trial participants who achieved MPR and an EFS of at least 12 months. Non-responders comprise n = 7 pre-selected patients who neither achieved MPR nor an EFS of 12 months. Each dot represents a single cell, color-coded by cell cluster.
b, Volcano plot representing the false discovery rate (-log10FDR) as a function of fold change (FC, log2FC) of major immune cell lineages identified in mass cytometry as calculated by edgeR. Each dot denotes a lineage.
c, Proportion of FlowSOM clusters (rows) within all cells of individual pre-selected patients (columns) post-neoadjuvant chemoimmunotherapy (TP3, TP1 see Extended Data Fig. 4f). Clusters were arranged by their FDRs (right bar graph).
d, Frequency of the CD8 T PD-1+ cluster within CD8+ T cells of responders (blue, n = 8) and non-responders (red, n = 7) at TP1 and TP3 in peripheral blood.
e, CD4 CD57+ cluster within CD4+ T helper (Th) cells of responders (blue, n = 8) and non-responders (red, n = 7) at TP1 and TP3 in peripheral blood. f, Median TIM-3 dual counts in cDC cluster of responders (blue, n = 8) and non-responders (red, n = 7) at TP1 and TP3 in peripheral blood.
g, Percentage of proliferating (Ki-67+) cells within CD39+ PD-1+ CD8+ T cells in peripheral blood of patients with EFS " 12 months (blue) and EFS < 12 months (red) as assessed via spectral flow cytometry, n = 36/6 (TP1), 40/10 (TP2), 28/10 (TP3) and 32/6 (TP4).
h, TIM-3 geometric mean fluorescence intensity (gMFI) in Lin (CD3 CD19 CD56 CD14)- CD11c+ HLA-DRhigh cDC in patients with EFS " 12 months (blue) and EFS < 12 months (red), n = 37/8 (TP1), 42/12 (TP2), 32/10 (TP3) and 33/6 (TP4). Bars represent means +SEM (d-h). Statistical testing with two-way ANOVA and uncorrected Fisher’s least significant difference (LSD) test (d-f) or a mixed-effects model (restricted maximum likelihood, REML) with Tukey’s multiple comparisons test (g, h). p values below 0.1 are shown.
3.6 Post-treatment circulating Ki-67-expressing CD39+ PD-1+ CD8+ T cells are associated with improved EFS
Fig. 5. CyTOF and spectral flow cytometry of PBMCs pre- and post-Neoadjuvant chemoimmunotherapy.
a, Uniform Manifold Approximation and Projection (UMAP) of mass cytometry data of PBMCs post-neoadjuvant chemoimmunotherapy (TP3) in selected patients (n = 50’000 randomly selected cells). Responders comprise n = 8 pre-selected SAKK 16/14 trial participants who achieved MPR and an EFS of at least 12 months. Non-responders comprise n = 7 pre-selected patients who neither achieved MPR nor an EFS of 12 months. Each dot represents a single cell, color-coded by cell cluster.
b, Volcano plot representing the false discovery rate (-log10FDR) as a function of fold change (FC, log2FC) of major immune cell lineages identified in mass cytometry as calculated by edgeR. Each dot denotes a lineage.
c, Proportion of FlowSOM clusters (rows) within all cells of individual pre-selected patients (columns) post-neoadjuvant chemoimmunotherapy (TP3, TP1 see Extended Data Fig. 4f). Clusters were arranged by their FDRs (right bar graph).
d, Frequency of the CD8 T PD-1+ cluster within CD8+ T cells of responders (blue, n = 8) and non-responders (red, n = 7) at TP1 and TP3 in peripheral blood.
e, CD4 CD57+ cluster within CD4+ T helper (Th) cells of responders (blue, n = 8) and non-responders (red, n = 7) at TP1 and TP3 in peripheral blood.
f, Median TIM-3 dual counts in cDC cluster of responders (blue, n = 8) and non-responders (red, n = 7) at TP1 and TP3 in peripheral blood.
g, Percentage of proliferating (Ki-67+) cells within CD39+ PD-1+ CD8+ T cells in peripheral blood of patients with EFS ≥ 12 months (blue) and EFS < 12 months (red) as assessed via spectral flow cytometry, n = 36/6 (TP1), 40/10 (TP2), 28/10 (TP3) and 32/6 (TP4).
h, TIM-3 geometric mean fluorescence intensity (gMFI) in Lin (CD3 CD19 CD56 CD14)- CD11c+ HLA-DRhigh cDC in patients with EFS ≥ 12 months (blue) and EFS < 12 months (red), n = 37/8 (TP1), 42/12 (TP2), 32/10 (TP3) and 33/6 (TP4). Bars represent means +SEM (d-h). Statistical testing with two-way ANOVA and uncorrected Fisher’s least significant difference (LSD) test (d-f) or a mixed-effects model (restricted maximum likelihood, REML) with Tukey’s multiple comparisons test (g, h). p values below 0.1 are shown.
Extended Data Fig. 4. CyTOF of PBMCs pre- and post-neoadjuvant chemoimmunotherapy.
a, Pre-gating strategy to exclude EQ normalization beads, identify single (uniform Gaussian parameters) and living (103Rh negative) cells.
b, Marker (columns) expression across clusters (rows) identified by FlowSOM as dual mean counts.
c, CD8+ T PD-1+ and CD4+ T CD57+ clusters among total PBMCs in responders (n = 7, blue) and non-responders (n = 8, red) assessed via mass spectrometry. Bars show means +SEM. Statistical testing with two-way ANOVA and uncorrected Fisher’s least significant difference (LSD) test.
d, tSNE contour plot of mass cytometry data (n = 5’596’279 cells), color-coded by cell cluster.
e, UMAP of PBMCs at TP1 and TP3 of responders (EFS ≥ 12 months, MPR, n = 8) and non-responders (EFS < 12 months, no MPR, n = 7). Each dot represents a single cell, color-coded by cell cluster, n = 50’000 randomly selected cells. f, Proportion of FlowSOM clusters (rows) within all cells of individual pre-selected patients (columns) at baseline (TP1). Clusters were arranged by their FDRs (right bar graph).
Extended Data Fig. 5. Spectral flow cytometry of PBMCs pre- and post-neoadjuvant chemoimmunotherapy.
a, Gating strategy to identify immune cell subsets in spectral flow cytometry data. Parent gate is indicated in top left corner.
b, Volcano plot representing the false discovery rate (-log10FDR) as calculated by edgeR as a function of fold change (FC, log2FC) of major immune lineages identified in spectral flow cytometry analyses at TP3. Each dot denotes a lineage. EFS ≥ 12 months, n = 32, EFS < 12 months, n = 10.
c, CD39+ PD-1+ CD8+ T cells in total PBMCs stratified by EFS ≥ 12 months and < 12 months, n = 37/6 (TP1), 41/11 (TP2), 30/10 (TP3), 32/6 (TP4).
d-e, CD57+ cells in CD4+ T cells (d) and CD57+ CD4+ T cells in total PBMCs (e) of patients stratified by EFS ≥ 12 months and < 12 months, n = 39/8 (TP1), 42/12 (TP2), 32/10 (TP3) and 33/6 (TP4).
f, TIM-3 geometric mean fluorescence intensity (gMFI) within CD141+ cDC1 stratified by EFS ≥ 12 months and < 12 months, n = 12/3 (TP1), 31/8 (TP2), 22/7 (TP3) and 21/5 (TP4).
g, TIM-3 gMFI within CD1c+ cDC2 stratified by EFS ≥ 12 months and < 12 months, n = 37/8 (TP1), 42/12 (TP2), 32/10 (TP3) and 33/6 (TP4).
c-g, Patients with EFS ≥ 12 months in blue, patients with EFS < 12 months in red. Bars represent means +SEM, statistical testing with mixed-effects model (restricted maximum likelihood, REML) with Tukey’s multiple comparisons test. p values below 0.1 are shown.
3.7 Serum CCL15 correlates with sustained therapeutic benefit in a small subset of patients
Fig. 6. Association of cytokine and chemokine patterns with response.
a, Multidimensional scaling (MDS) analysis of global serum cytokine and chemokine levels of participants in the SAKK 16/14 trial, color-coded by EFS and shape coded by timepoint: < 12 months (red), n = 12 (TP1/TP3), blue ≥ 12 months (n = 42 (TP1), 36 (TP3)).
b, Post-treatment (TP3) serum concentrations of ten cytokines (TP1 in Extended Data Fig. 6c), n = 36 (EFS ≥ 12 months) and 12 (EFS < 12 months).
c, Representative flow cytometry plots of CCR1 and Ki67 expression within CD39+ PD-1+ CD8+ T cells in peripheral blood from two standout responders exhibiting elevated CCL15 serum levels.
d, Percentage of proliferating (Ki-67+) CCR1-expressing cells among CD39+ PD-1+ CD8+ T cells comparing patients with EFS ≥ 12 months and high (n = 2, yellow) or low (n = 4, blue) CCL15 serum, and patients with EFS < 12 months (n = 4, red). Bars represent means +SEM (b, c). Statistical testing with unpaired t test (b) or two-way ANOVA and uncorrected Fisher’s least significant difference (LSD) test (d). p values below 0.1 are shown.
Extended Data Fig. 6. Analysis of serum cytokines and chemokines in SAKK 16/14 participants.
a, Cytokines with differences between responders (EFS ≥ 12 months, MPR, n = 10, blue) and non-responders (EFS < 12 months, no MPR, n = 9, red) and between timepoints.
b, Cytokines with differences between responders (EFS ≥ 12 months, MPR, n = 10, blue) and non-responders (EFS < 12 months, no MPR, n = 9, red), pooled for timepoints.
c, Pre-treatment (TP1) serum concentrations of 10 cytokines correlated with response in patients achieving 12 months EFS (n = 42 at TP1, n = 36 at TP3) or not (n = 12 at TP1 and TP3). Bars represent means +SEM (a-c). Statistical testing with two-way ANOVA and uncorrected Fisher’s least significant difference (LSD) test (a), Mann-Whitney test (b) or unpaired t test (c). p values below 0.2 are shown.
Extended Data Fig. 7. Analysis of serum cytokines and chemokines in SAKK 16/14 participants.
a, tSNE plot of n = 10,000 randomly selected cells from lung tumors reported in Lambrechts et al, 201834. Left, plot is color coded for cluster annotation. Right, expression of indicated genes across all cells.
b, tSNE of n = 10,000 randomly selected cells isolated from NSCLC tissue, annotated by cluster (right) and with CCL15 expression (left) from Wu et al, 202136.
c, tSNE of n = 10,147 cells isolated from NSCLC tissue, annotated with clusters (right) and with CCL15 expression (left) from Song et al, 201935.
d, Right, weighted network analysis showing relative signaling intensity between cell populations from Lambrecht et al, 201834. Right, depiction of two representative signaling axes, namely CCL15 from epithelial cells to CCR1 on CD8+ T cells and from LGALS9 (encoding for Galectin-9) on myeloid cells to HAVCR2 (encoding for TIM-3) on CD8+ T cells.
e, tSNE plots of n = 24,911 T cells, color-coded by their original cluster annotation (right) or CCR1 expression (left) according to Lambrechts et al, 201834.
f, tSNE plot of n = 12’851 T cells, color-coded by consensus clusters (right) and with CCR1 expression (left) from Liu et al, 202237.
g, Every bar shows the population size of a single TCR clone from Liu et al, 202237, stratified by the type of lesions it was isolated from: treatment naïve samples (grey), responsive post-therapy samples (blue) and non-responsive post-therapy samples (red). Transparency indicates CCR1 expression.
h, Flow cytometry analysis of CCR1 expression by intratumoral T cells from a different NSCLC patient cohort (n = 4).
