Abstract 4745
Background
Different cancers subtypes can often be effectively treated with similar Rx classes, i.e., with PARPi in the case of BRCA mutations or with anti-PD1/PDL1 inhibitors for patients with mismatch repair deficiencies. Using the Epic Sciences Functional Cell Profiling (FCP) platform utilizing high resolution digital pathology and machine learning, we analyzed ∼100,000 single CTCs from multiple indications to index metastatic cancers, with the longer-term hypothesis that sub-classification of CTCs may help define therapeutic decisions across cancer types.
Methods
92,300 CTCs underwent FCP analysis (single cell digital pathology features of cellular and sub-cellular morphometrics) were collected from cancer patients comprising Prostate (1641 pts, 70,747 CTCs), Breast (268 pts, 8,718 CTCs), NSCLC (110 pts, 1884 CTCs), SCLC (141 pts, 8,872 CTCs) and Bladder (65 pts, 2079 CTCs). An equal number of CTCs from each indication was sampled to create a balanced cohort for training. K-means clustering was applied on the training set and an optimized number of clusters were determined using the elbow approach. After defining the clusters on the training set, the cluster centers were extracted from k-means, and used to train a k-Nearest Neighbor (k-NN) classifier to predict the cluster assignment for the remaining CTCs designated as the test set.
Results
The optimized number of clusters was 9. Breast cancer CTCs were more enriched with higher CK expressing CTCs (c1), while SCLC and some of mCRPC shared similar small cell like features (c5). Cluster subtypes and characteristics are listed.Table:
1984PD
| Cluster | Characteristics | Bladder | Breast | NSCLC | SCLC | Prostate |
|---|---|---|---|---|---|---|
| c1 | Highest CK | 8% | 24% | 1% | 5% | 9% |
| c2 | Highest N/C ratio | 13% | 15% | 7% | 35% | 21% |
| c3 | Large, Elongated | 1% | 3% | 12% | 1% | 3% |
| c4 | Low N/C, Low CK | 33% | 24% | 25% | 8% | 12% |
| c5 | Small, Higher N/C ratio, More Round | 11% | 12% | 11% | 37% | 33% |
| c6 | Low CK, Elongated | 8% | 4% | 8% | 2% | 4% |
| c7 | Highest Nucleoli Count | 0% | 0% | 17% | 1% | 4% |
| c8 | Large Nuclear Area, Highest Nuclear Entropy | 21% | 14% | 10% | 11% | 10% |
| c9 | Most Elongated Cytoplasm | 4% | 3% | 9% | 2% | 4% |
Conclusions
Heterogeneous CTC phenotypic subtypes were observed across multiple indications. Each indication harbored subtype heterogeneity and shared clusters with other cancers. Patient cluster subtype analysis for prognosis and therapy benefit as well as linkage of CTC subtype genotypes to patient survival by cancer type is ongoing.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Epic Sciences.
Disclosure
A. Jendrisak: Full / Part-time employment: Epic Sciences. R. Sutton: Full / Part-time employment: Epic Sciences. S. Orr: Full / Part-time employment: Epic Sciences. D. Lu: Full / Part-time employment: Epic Sciences. J. Schonhoft: Full / Part-time employment: Epic Sciences. Y. Wang: Full / Part-time employment: Epic Sciences.
Resources from the same session
Poster Discussion session - Basic science - Invited Discussant 8PD, 1983PD and 1984PD
Presenter: Thomas Helleday
Session: Poster Discussion session - Basic science
Resources:
Slides
Webcast
Poster Discussion session - Basic science - Invited Discussant 9PD, 1985PD and 1986PD
Presenter: Joan Seoane
Session: Poster Discussion session - Basic science
Resources:
Slides
Webcast