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Cancers display significant heterogeneity with respect to tissue of origin, driver mutations, and other features of the surrounding tissue. It is likely that individual tumors engage common patterns of the immune system-here "archetypes"-creating prototypical non-destructive tumor immune microenvironments (TMEs) and modulating tumor-targeting. To discover the dominant immune system archetypes, the University of California, San Francisco (UCSF) Immunoprofiler Initiative (IPI) processed 364 individual tumors across 12 cancer types using standardized protocols. Computational clustering of flow cytometry and transcriptomic data obtained from cell sub-compartments uncovered dominant patterns of immune composition across cancers. These archetypes were profound insofar as they also differentiated tumors based upon unique immune and tumor gene-expression patterns. They also partitioned well-established classifications of tumor biology. The IPI resource provides a template for understanding cancer immunity as a collection of dominant patterns of immune organization and provides a rational path forward to learn how to modulate these to improve therapy.

Microbial community analysis can be biased by multiple technical factors, such as storage conditions, DNA extraction, or amplification conditions. In a high-throughput laboratory that relies on samples obtained from thousands of different subjects, knowledge of the extent of subject-introduced sampling and storage variation on the outcome of the inferred microbiome, as well as the effect of laboratory-introduced variation caused by reagent batches, equipment, or operator on the consistency of these processes within the laboratory is paramount. Here, we analyzed the effect of sampling from different parts of the same stool specimen or on different consecutive days, as well as short-term storage of samples at different temperatures on microbiome profiles obtained by 16S rRNA gene amplification. Each of these factors had relatively little effect on the microbial composition. In addition, replicate amplification of 44 stool samples showed reproducible results. Finally, 363 independent replicate extractions and amplifications of a single human homogenized stool (HS) specimen showed reproducible results (average Lin’s correlation = 0.95), with little variation introduced by HS batch, operator, extraction equipment, or DNA sequencer. In all cases, variations between replicates were significantly smaller than those between individual samples; subject identity always was the largest determinant. We propose that homogenized stool specimens could be used as quality control to routinely monitor the laboratory process and to validate new methods.

The composition of the vaginal microbiome, including both the presence of pathogens involved in sexually transmitted infections (STI) as well as commensal microbiota, has been shown to have important associations for a woman’s reproductive and general health. Currently, healthcare providers cannot offer comprehensive vaginal microbiome screening, but are limited to the detection of individual pathogens, such as high-risk human papillomavirus (hrHPV), the predominant cause of cervical cancer. There is no single test on the market that combines HPV, STI, and microbiome screening. Here, we describe a novel inclusive women’s health assay that combines self-sampling with sequencing-based HPV detection and genotyping, vaginal microbiome analysis, and STI-associated pathogen detection. The assay includes genotyping and detection of 14 hrHPV types, 5 low-risk HPV types (lrHPV), as well as the relative abundance of 32 bacterial taxa of clinical importance, includingLactobacillus,Sneathia,Gardnerella, and 4 pathogens involved in STI, with high sensitivity, specificity, and reproducibility. For each of these taxa, healthy ranges were determined in a group of 50 self-reported healthy women. The hrHPV portion of the test was evaluated against the Digene High-Risk HPV HC2 DNA test with vaginal samples obtained from 185 women. Results were concordant for 181/185 of the samples (overall agreement of 97.83%, Cohen’s kappa = 0.93), with sensitivity and specificity values of 94.74% and 98.64%, respectively. Two discrepancies were caused by the Digene assay’s known cross-reactivity with low-risk HPV types, while two additional samples were found to contain hrHPV not detected by Digene. This novel assay could be used to complement conventional cervical cancer screening, because its self-sampling format can expand access among women who would otherwise not participate, and because of its additional information about the composition of the vaginal microbiome and the presence of pathogens.