Drug Resistance and Transcriptomics

Our database lists more than 100 studies in which drug resistant cells were compared against drug sensitive cells. Most commonly, a sensitive cell line is passaged in the presence of low drug levels until a resistant strain emerges, whereupon a transcriptomic comparison can be made. In other cases, tumor cells from resistant patients may be compared with cells from sensitive patients.

Given this plethora, we decided to gather all these studies and see if any particular genes emerged that were commonly upregulated or downregulated in the case of drug resistance. The result is a bit more complex than we had hoped. We looked at 95 studies, excluding those involving “radioresistance.” The gene that was most commonly altered in these studies was OAS1, appearing 21 times out of 190 opportunities (all 95 studies have up and down-regulated portions). That seems nice…a “big name” gene popping up at a frequency that, without crunching the numbers, appears to be significant. The problem is this: OAS1 appeared in both the resistance-upregulated and resistance-downregulated datasets (13 times up and 8 times down, to be specific). Therefore, we can’t make the blanket statement that OAS1 is upregulated in cases of drug resistance, nor can we surmise that suppressing the innate immune response (OAS1 is a big player there, after all) might overcome drug resistance. OAS1 is not unique in this respect.

That doesn’t mean that generation of lists of commonly up- and down-regulated genes involved in drug resistance would be entirely fruitless and couldn’t possibly spur insight. We’ve given these two lists the database IDs 129091122 and 129092122. In both cases, a gene had to occur at least 7 times (out of 95) to make the list, giving the lists a composition of 163 and 119 genes. 26 genes were found in both lists:

AREG	IFI27
C1orf24	IL1A
CA12	KYNU
CD24	LCN2
CEACAM6	OAS1
CXCR4	PEG10
DUSP6	SERPINB2
EMP1	SERPINE2
FAM129A	SOCS2
FSTL1	STC2
GPNMB	TSPAN8
HLA-DRA	UCHL1
HLA-DRB1	VCAN

Ignoring the fact that multiple genes are found in both lists, what broad categories of genes intersect with these lists? In the case of upregulation, the innate immune response does indeed seem relevant (e.g. genes upregulated early (vs late) in HCMV infection intersect with a P-value of 10^-76). Erlotinib and neo-adjuvant therapy seem to do a fine job of upregulating common drug-resistance genes; if borne out in the lab/clinic, this would have obvious implications for cancer cocktail approaches. On the downregulation side, the metastatic (or not) nature of underlying tissues seems to be relevant. Specifically, transcripts downregulated in aggressively metastatic tissue overlap with transcripts that are downregulated in the case of drug resistance. For far deeper details, just plug either of the above dbase IDs into our “Fisher” app.

How about creating lists of, say, upregulated genes that weren’t found at all in the downregulated category? We tried that, but were met with discouragement. We found that RAB25 was the single best example of a transcript that was downregulated (in 8 studies), but never upregulated. Searching for validation of this characteristic of RAB25 in specific studies, the first study we stumbled upon was this: RAB25 confers resistance to chemotherapy by altering mitochondrial apoptosis signaling in ovarian cancer cells. There, it seems, RAB25 upregulation, not downregulation, correlates with resistance. Hmmmm.

Apparently, the same transcript may be upregulated in one resistance study, and downregulated in the next. My background in this field (a couple distant lectures and/or presentations) informed me that a handful of transporters are the main culprits in drug resistance, and I had hoped that this phenomenon would be obvious once multiple studies were compounded. This is not the case.

One might think that the above conundrum be resolved by examining particular drugs. That is, certain critical transcripts would always be upregulated in resistance to a particular drug. Cisplatin-resistance is the most commonly studied sort of resistance. Here, of 5 genes found in 4 out of 6 of the cisplatin-resistance studies we have on hand, 3 are both upregulated and downregulated, depending on the study: QPCT, SAA1, and MMP1 (TGFB2 is up in all four cases, and ANO1 is always down).

To attempt to clarify matters, we clustered the 190 datasets. Specifically, we performed Fisher’s exact test for each dataset against our entire database, generating millions of P-values. These P-values were the raw data for clustering (Cluster 3.0, k-means). 10 clusters were generated. The top genes found in each cluster are now found in our database with IDs 129137122, 129138122, 129139122, 129140122, 129141122, 129142122, 129143122, 129144122, 129145122, and 129146122.

Though the clusters did not nicely segregate according to drugs or cell types, as one might desire, some clarity was gained. Bearing in mind that both up- and down-regulated transcripts can be found in a single cluster, Cluster 0 transcripts tend to be upregulated on innate immune stimulation (e.g. via interferons). Cluster 3 transcripts tend to be upregulated in the case of metastasis and are enriched for cell-surface markers, while cluster 5 and 8 genes tend to be downregulated in metastatic cells. Cluster 6 genes have a strong tendency to be upregulated in cancer versus adjacent tissue and, rather bizarrely, downregulated on resveratrol treatment (P = 10^-56). Other clusters are more nuanced. Thus, it would appear that investigators might wish to place special relevance on the status of cells with regard to innate immunity and metastasis when considering approaches that might mitigate drug resistance. Depending on the cluster, we see hints that particular drugs could, to some extent, reverse drug resistance: bromodomain inhibitors, noggin, losartan, gefitinib, etc. Other drugs, of course, could enhance drug resistance.

It is sometimes difficult to trust the output of clustering programs, so I performed an eyeball version of clustering in Excel. Give different colors to different significance levels (below, green indicates P<10^-15), and then sort a column. Gather all columns where colors (indicating significance) percolated to the top. That’ll be cluster 1.Then move on to a column that doesn’t fall into cluster 1. Repeat. Believe it or not, this crude method matched up quite nicely with the software I used. To me, this sort of correspondence between the mathematical perfection of the clustering software and the childish simplicity of matching columns that have the same colors indicates that maybe we shouldn’t spend an excess amount of time/energy debating the merits of, say, “Euclidean distance” vs. “City block distance.” Below is a sliver of the result:

Again, for the fine details, just visit WhatIsMyGene, plug in database IDs (or your own datasets), and have fun.

Note 2/22/2022: We've added quite a few more studies involving resistance to our database. At this point, it's fairly obvious that genes upregulated in cells resistance to one sort of treatment may actually be downregulated on resistance to another treatment. This observation dampens hopes for across-the-board approaches to drug resistance. On the positive side, it may mean that resistance could be dealt with via drug cocktails; i.e. two drugs that trigger opposing resistance patterns could be combined in a treatment. At some point in the future, we'll re-cluster our resistance results. We'll be a bit more rigorous about finding an optimal number of clusters, look a bit deeper into commonalities in these clusters, and perhaps examine cases where 2 "resistance-complementary" drugs might be applied to particular maladies.

Note 9/17/2022: A quote from Comparative proteomic analysis identifies key metabolic regulators of gemcitabine resistance in pancreatic cancer: Surprisingly, a number of proteins that were downregulated in MIA-GR8 cells have been reported to promote drug resistance in other cancer types. It's nice to validate our view above, but it's also disappointing to see that many researchers may still be stuck in a one-dimensional view of drug resistance.

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