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163 A multi-modal, pan-cancer atlas of tumor-immune states across primary and metastatic disease using a large, real-world database
  1. Prerna Jain1,
  2. Michelle M Stein2,
  3. Paul Fields2,
  4. Bolesław Osinski2,
  5. Luca Lonini2,
  6. Ariane Lozac’hmeur2,
  7. Rohan Joshi2,
  8. Halla Nimeiri2,
  9. Martin Stumpe2,
  10. Kate Sasser2,
  11. Catherine Igartua2,
  12. Justin Guinney2 and
  13. Mary L Disis3
  1. 1Tempus Labs, San Diego, CA, USA
  2. 2Tempus Labs, Inc, Chicago, IL, USA
  3. 3University of Washington, Seattle, WA, USA
  • Journal for ImmunoTherapy of Cancer (JITC) preprint. The copyright holder for this preprint are the authors/funders, who have granted JITC permission to display the preprint. All rights reserved. No reuse allowed without permission.


Background Immuno-oncology (IO) therapies have demonstrated effective and durable benefits in multiple cancer indications but responses are variable. Discovery and validation of better biomarkers of treatment response, ability to modulate immunological states, selection of optimal drug combinations, and identification of new IO targets are top priorities to maximize the clinical impacts of immunotherapy. While the immune and tumor microenvironment have been well-characterized in primary tumors,1 large-scale assessments in metastatic disease are lacking. Here, we conducted a multi-modal, pan-cancer analysis comparing immune states across primary and metastatic disease using a real-world database.

Methods Analysis was conducted using the Tempus IO platform, encompassing molecular data and immunophenotyping algorithms layered upon a large, real-world patient database. To date, the platform comprises over 200k de-identified records with pre-computed immunophenotypes (e.g. immune cell fraction, HLA typing and quantification, TCR/BCR diversity, TIL estimation from H&E) across dozens of cancer types, with over 20k cases treated with IO therapy. Somatic DNA alterations and whole-exome transcriptomes were profiled using the Tempus xT and xR NGS assays, respectively, with optimized probes for TCR/BCR profiling.

Results A landscape, multi-modal comparison of tumor immune states was performed on 70k de-identified patient records, comprising a diverse spectrum of cancer types, tissue sites, and prognostic stages. Analyses of bulk tumor RNA showed liver mets with significantly lower immune infiltration, higher macrophage fraction, and lower TCR diversity compared to primary and lung met tumors (P<0.001) (figure 1). TIL quantification from 104 NSCLC digitized H&E slides revealed a significant correlation between intratumoral TIL density and cytotoxic T-cell score (ρ=0.57, P<0.001) (figure 2A), and confirmed lower intratumoral TIL density in liver mets versus primary lung (P<0.01) (figure 2B). In a stratified Cox regression analysis of multiple cancer types treated with first-line immune checkpoint blockade (N=1623), IO-response signatures2–4 were highly predictive of real-world OS (P<0.001)(figure 3A), were superior to TMB, and exhibited significant differential enrichment across primary and metastatic sites (P<0.001) (figure 3B).

Conclusions In the largest pan-cancer analysis to date, significant differences in tumor immune states were observed by primary and metastatic sites, and by indication, with important implications for IO-treatment strategies. These data provide valuable and timely insights for IO research, where simultaneous assessment of molecular, immune, and clinical traits is required.


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  3. Lau D, Khare S, Stein MM, Jain P, Gao Y, BenTaieb A, et al. Integration of tumor extrinsic and intrinsic features associates with immunotherapy response in non-small cell lung cancer. Nat Commun. 2022;13:4053.

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Ethics Approval Analyses were performed using de-identified data under the exemption Pro00042950 granted by the Advarra, Inc. Institutional Review Board (IRB). Per this exemption, written informed consent was not required. All methods were carried out in accordance with relevant guidelines and regulations.

Abstract 163 Figure 1

Immune cell states across indications and primary/met sites. (A) Immune cell deconvolution of bulk RNA highlights significant differences in estimated immune cell proportions across primary and metastatic sites. (B) Shannon’s entropy of TCR beta repertoires, with higher values indicating a more diverse repertoire. Asterisk denotes significant differences in entropy (kruskal-wallis p < 0.001)

Abstract 163 Figure 2

Intratumoral TILs quantification. (A) Example of HE image tissue segmentation and cell classification model outputs that are combined to compute TIL density. (Left) Whole slide image of HE stained tissue. (Middle) Overlay of tumor and stroma regions detected by tissue segmentation model. (Right) Zoom showing lymphocytes and tumor cells detected by cell classification model. Lymphocytes are seen infiltrating tumor and stroma regions. (B) Distribution of intra-tumoral TIL density in NSCLC by tumor site (N=104)

Abstract 163 Figure 3

Association of NGS-based measures of immune activity in IO-treated patients. (A) Cox proportional hazards regression of real-world overall survival in patients (n=1623) treated with first line immune checkpoint blockade, stratified by cancer type and regimen class. (ICB monotherapy or ICB in combination with chemotherapy). Each row represents a single stratified Cox PH model result for the selected RNA-based feature or TMB (calculated from DNA). (B) Immune activity score distributions by cancer type, and primary/metastatic site. Asterisks denote significant differences (kruskal-wallis P < 0.001).

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