Enhancing Tumor Content through Tumor Macrodissection

FFPE tissues are frequently heterogeneous admixtures of tumor and non-tumor tissues. High sensitivity genomic tests are becoming increasingly prevalent in both clinical and research settings but can be confounded by the presence of contaminating non-tumor tissue. Indeed, a minimum tumor content of 60% is frequently recommended for genomic studies. Percentage tumor can be determined by the area of tissue occupied by the tumor material or by the proportion of tumor cells within the tissue. Although tumor by area is a commonly used metric for tumor purity, it does not always portray an accurate description of the tissue. Consider two tissues, both with 1000 cells, of which 500 are tumor cells. In tissue A, the 500 non-tumor cells are stromal cells with similar volumes to that of the tumor cell. In this tissue, the percentage of tumor can be considered 50% by both cellularity and area. In tissue B, the 500 non-tumor cells are fat cells with volumes that are 4x that of the tumor cell. In this tissue, the percentage tumor is still 50% by cellularity but 20% by area. A third tissue, tissue C, is composed of 500 tumor cells plus 400 fat cells and 800 stromal cells with volumes that are 4x and 0.5x that of the tumor cells, respectively. Given 100 fat cells equals the volume of 800 stromal cells, the percentage tumor of tissue C is 29% by cellularity (500/1700) but still 20% by area. Tissue D is also comprised of the tumor, fat, and stromal cells with volume ratios of 1x, 4x, and 0.1x. However, the number of cells is 400, 10, and 720, respectively. Thus, the percentage tumor of tissue D is 35% by cellularity (400/1130) but 78% by area. These examples are overly simplistic and do not reflect real-world tissue compositions but clearly convey the importance of tissue composition and the difference between tumor content by area and by cellularity. Importantly, when it comes to enriching tumor content for downstream nucleic acid extraction, tumor cellularity is the more important attribute due to the heightened confounding potential of extracting genomic material from more non-tumor cells than tumor cells. This not only underscores the need to assess the tumor content of tissues in terms of percentage cellularity but also the need to excise unwanted tissue in order to minimize any potential negative effects of the non-tumor tissue. There are several methods available for tissue enrichment, with the main ones being macrodissection and microdissection.

Macrodissection, the method described in this protocol, is relatively quick, simple, and does not require costly or specialized equipment. Although macrodissection can greatly improve tumor content, it is important to understand that it does not completely eliminate non-tumor material. The purpose of macrodissection is to enrich the tissue of interest sufficiently through the exclusion of unwanted tissue in order to reduce the “noise” stemming from unwanted tissue, which in turn can enhance the signal of interest from the tissue of interest. Thus, macrodissection mediated tumor enrichment is a way to enhance the signal-to-noise ratio in order to better detect markers of interest, particularly tumor-specific molecular markers with low abundance or poor expression. However, macrodissection has limitations due to the lack of precision offered by coarse tools such as razor blades and is susceptible to precision issues stemming from the line thickness of the pathologist’s marker, as well as potential errors when tracing the pathologists H&E demarcations. As alluded above, it is not possible to achieve 100% tumor purity due to the presence of inherent and tumor-inducing stromal elements (i.e., connective tissue, stromal fibroblasts, blood vessels, benign reactive lymphocytes, macrophages) embedded within the tumor itself. Indeed, many invasive or diffusely infiltrative malignancies induce a robust desmoplastic stromal response, resulting in clusters of tumor cells that are intimately admixed with stromal fibroblasts and other non-neoplastic cell types; where tumors associated with this stromal reaction pattern, such as pancreatic cancer tissues²¹, may benefit more from digitally guided microdissection rather than manual macrodissection.

Manual microdissection is performed under a microscope to aid identification, dissection, and isolation of tissues specific cells or populations using a needle or scalpel and has the advantage of increased precision over macrodissection²². However, manual microdissection is a laborious process that lacks the finesse needed for complex tissues with low tumor contents or intricate features that are incompatible with manual dissection. Such tissues can be dissected using high-precision automated methods like laser capture microdissection. Indeed, digitally guided microdissection has been shown to yield higher percentage tumor contents compared to manual macrodissection in pancreatic cancer tissues²³. However, the drawbacks of these high precision automated methods, such as the need for specialized, expensive equipment and highly trained individuals, have hindered its incorporation into workflows. A study by de Bruin et al. comparing the effects of macrodissection and laser capture microdissection (LCM) on gene expression profiling found that LCM samples had low total RNA yields (30 ng average) and required two rounds of mRNA amplification in order to meet the cDNA library prep input threshold²⁴. The authors found that the resulting LCM gene expression profiles were affected by the rounds of mRNA amplification more than macrodissected profiles were affected by non-tumor stromal contributions and concluded that macrodissection could be adequately used to generate reliable gene expression data²⁴.

A significant advantage of NanoString digital gene expression profiling, particularly when working with highly degraded FFPE derived RNA, is that it does not require enzymatic dependent processes such as RNA amplification or preparation of cDNA libraries. However, assays are typically optimized for inputs between 50-300 ng of total RNA^25,26, which, based on the findings of de Bruin et al.²⁴, may not be compatible with microdissected tissues without increasing the tissue input; an unfavorable demand in an era where tissue samples are increasingly collected as small biopsies rather than surgical resections. The RNA inputs used for the DLBCL90 assay ranged from 68.5-300 ng for both the macrodissected and non-dissected tissues. The results show that macrodissection resulted in call changes in 60% of the samples examined and that these changes were observed irrespective of RNA input of the macrodissected samples. However, the COO probability for the low RNA input did encroach the COO GCB/UNC probability call threshold, where the thresholds are 0 to <0.1 for GCB, 0.1-0.9 for UNC, and >0.9 to 1.0 for ABC calls²⁰. The principal DLBCL COO subtypes are GCB and ABC, which make up 41% and 44% of all DLBCL cases, with UNC representing an intermediate group of the two and ABC being the most aggressive^20,27. Thus, while the COO call change upon macrodissection of sample C did not cause a frank change in COO subtype from GCB to ABC, the change from GCB to UNC may suggest a shift towards a more aggressive disease. Moreover, recent studies indicate that the UNC subtype is not simply just an intermediate subtype and that it may potentially possess subtype-specific therapeutically exploitable attributes²⁸. Similarly, macrodissection of samples A and E did not cause frank changes in DHITsig calls from DH negative to DH positive, or vice versa. However, the movements of a GCB sample (sample A) from NEG to UNCLASS and an ABC sample (sample E) from UNCLASS to NEG upon macrodissection are biologically appropriate as double hit translocations involving BCL2 are reported to be an exclusively GCB phenomenon¹⁹. Although translocations are traditionally and ubiquitously detected by FISH in clinical settings, there is a growing momentum to identify an alternative less involved and time-consuming method for their detection. The DLBCL90 assay is an important tool that addresses this need, where the rationale for its use is strengthened by the finding that this assay is capable of detecting translocations cryptic to FISH probes used in clinical diagnostics²⁹.

The macrodissection protocol described above outlines a simple method that enables researchers to increase the tumor content of tissue samples that would ordinarily fall below commonly used study inclusion criteria thresholds. Including macrodissection in a study workflow enables researchers to salvage poorly tumor-dense tissues from study exclusion by increasing their tumor content. In turn, this permits increased confidence that the resulting RNA and DNA eluates represent the tumor under genomic investigation. Although other more precise methods for tissue dissection do exist, for tumors that grow in a more expansive, non-infiltrative, sheet-like, or solid fashion, macrodissection is likely sufficient. The results presented here highlight the importance of tumor purity in genomic assays and macrodissection as a reliable tool to achieve this.