It is increasingly obvious that tumors are highly heterogeneous at a molecular level, and this heterogeneity likely plays a critical role in their behavior and response to therapy. Consequently, the issue of sample size (and the number of cells scored in an assay) is one of critical importance for cytological specimens, mainly due to the risk of false-negatives in under-sampled or under-scored samples. Unfortunately, sample size is rarely addressed in a rigorous and probabalistic manner when developing ancillary studies.
An interesting online tool has been created by Christoph Hafemeister in the research laboratory of Rahul Satija that theoretically calculates the minimal number of cells needed (at a desired confidence level, e.g. 95%) to detect rare cells at a given frequency (e.g. 1% of the population). For example, in the case of a current ancillary test of importance to immunotherapy selection, PD-L1 immunohistochemistry in non-small cell lung carcinoma, a patient whose tumor displays no staining (<1%) is not eligible for pembrolizumab therapy, whereas a tumor that displays “any” staining is eligible for this therapy.
I created the following table using the online tool, making the assumption that there are only two cell types of interest (biomarker positive and negative) and that detection of a single positive cell in a sample defines positivity. Obviously, the percentage of target cells in a population is critical in determining the appropriate sample size.
Interestingly, the ASCO/CAP HER2 testing guidelines recommend the scoring of just 20 cells in ISH assays. The above analysis suggests that false-negative results may be produced if the HER2 amplified cells are present at less than 10% of the population. This is particularly relevant to fine-needle aspiration biopsy samples of metastatic lesions where the sample size may be low. Of course the biology and therapeutic response of tumors with rare biomarker-positive cells is always a question.
We are already in a more-with-less era, and expectations for biomarker assays on small core biopsies and FNAs continue to rise. Image analysis and deep learning tools may help with our accuracy, but they will have to be trained by pathologists to ensure sample adequacy.