Abstract PR006: Multi-modal single-cell profiling of sarcomas from archival tissue reveals mechanisms of resistance to immune checkpoint inhibitors
Bibliographic record
Abstract
Abstract Single-cell genomics is an enabling technology that may inform the molecular underpinnings of drug response and resistance in patient biopsies. These methods are difficult to implement in the study of rare diseases such as sarcomas due to specimen requirements and technical limitations. Here, we evolved novel methods that we recently reported in melanoma (Wang, Fan, et al., bioRxiv, 2022) which enable single-nucleus RNA, T cell receptor (snRNA/TCR-seq), and pool-matched whole-genome sequencing (WGS) from archival frozen sarcoma tissue. This enabled profiling of 75,716 cells and 788 matched TCR clonotypes from six patients with intimal sarcoma (INS) and undifferentiated pleomorphic sarcoma (UPS), including two matched pair samples from pre/post-immune checkpoint inhibitor (ICI). Our analysis revealed substantial transcriptional cancer cell heterogeneity driven by varying copy number alterations (CNAs). In one patient with INS with a complete response to ICI followed by an isolated recurrence, we identified a rare cancer cell clone defined by CNA (confirmed with WGS) and resulting transcriptional outputs that pre-existed and emerged during resistance. Furthermore, in a UPS patient with intrinsic resistance to ICI, we find adequate T cell clonal expansion, activation, and differentiation, suggesting appropriate T cell response to ICI dampened by intrinsic mechanisms of ICI resistance within the cancer cells. Non-negative matrix factorization (NMF) analysis identified cell states associated with either intrinsic or adaptive resistance to ICI that was distinct from resistance to doxorubicin. These observations are consistent with those previously reported from sequential biopsies obtained from KEYNOTE-001 in metastatic melanoma (Wang, Fan, et al., bioRxiv, 2022), which also revealed emergence of pre-existing populations of resistant clones defined by their underlying aneuploidy patterns. Together, these results demonstrate feasibility of implementing single-cell genomics from archival tissue to study sarcoma and propel our understanding of drug resistance. Conceptually, this work suggests that large-scale CNAs may allow us to identify cell sub-populations associated with ICI resistance in sarcoma and in other diseases. Citation Format: Sminu Bose, Blake Caldwell, Amit Dipak Amin, Johannes Melms, Somnath Tagore, Matthew Ingham, Benjamin Izar, Gary K. Schwartz. Multi-modal single-cell profiling of sarcomas from archival tissue reveals mechanisms of resistance to immune checkpoint inhibitors [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr PR006.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".