All my p-adj values are 1 when I call results and contrast "C" and "U." I am guessing that I have too few replicates (3 technical replicates) to observe statistically significant differences. In my PCAplot the samples were clustered (rather closely I may add) by sample rather than treatment which makes sense. Note the results from contrasting "T" and "U" did produce a couple statistically significant genes. Any ideas on how to handle/minimizing this problem?

colData
           sample_id    sample treatment
1  GA-Index01-ATCACG       C13         U
2  GA-Index10-TAGCTT       ES2         U
3  GA-Index11-GGCTAC       ES2         C
4  GA-Index12-CTTGTA       ES2         T
5  GA-Index02-CGATGT       C13         C
6  GA-Index03-TTAGGC       C13         T
7  GA-Index04-TGACCA     DOV13         U
8  GA-Index05-ACAGTG     DOV13         C
9  GA-Index06-GCCAAT     DOV13         T
10 GA-Index07-CAGATC     HeyC2         U
11 GA-Index08-ACTTGA     HeyC2         C
12 GA-Index09-GATCAG     HeyC2         T
13 GA–Index13-AGTCAA    OV2008         U
14 GA–Index23-GAGTGG    OVCAR4         U
15 GA–Index25-ACTGAT    OVCAR4         C
16 GA–Index27-ATTCCT    OVCAR4         T
17 GA–Index14-AGTTCC    OV2008         C
18 GA–Index15-ATGTCA    OV2008         T
19 GA–Index16-CCGTCC   OVCAR10         U
20 GA–Index18-GTCCGC   OVCAR10         C
21 GA–Index19-GTGAAA   OVCAR10         T
22 GA–Index20-GTGGCC    OVCAR8         U
23 GA–Index21-GTTTCG    OVCAR8         C
24 GA–Index22-CGTACG    OVCAR8         T
25 GA-Index02-CGATGT    OVCAR5         U
29 GA-Index07-CAGATC    OVCAR5         C
30 GA–Index19-GTGAAA    OVCAR5         T
31 GA-Index04-TGACCA    OVCAR7         U
32 GA-Index12-CTTGTA    OVCAR7         C
33 GA–Index16-CCGTCC    OVCAR7         T
34 GA-Index05-ACAGTG     SKOV3         U
35 GA-Index06-GCCAAT     SKOV3         C
36 GA–Index15-ATGTCA     SKOV3         T
37 GA-Index01-ATCACG     CaOV3         U
38 GA-Index10-TAGCTT TOV3133-D         U
39 GA-Index11-GGCTAC TOV3133-D         C
40 GA-Index12-CTTGTA TOV3133-D         T
41 GA-Index02-CGATGT     CaOV3         C
42 GA-Index03-TTAGGC     CaOV3         T
43 GA-Index04-TGACCA  OV3133-D         U
44 GA-Index05-ACAGTG  OV3133-D         C
45 GA-Index06-GCCAAT  OV3133-D         T
46 GA-Index07-CAGATC    TOV21G         U
47 GA-Index08-ACTTGA    TOV21G         C
48 GA-Index09-GATCAG    TOV21G         T
49 GA–Index13-AGTCAA     OV207         U
53 GA–Index14-AGTTCC     OV207         C
54 GA–Index15-ATGTCA     OV207         T
55 GA–Index16-CCGTCC    Cis200         U
56 GA–Index18-GTCCGC    Cis200         C
57 GA–Index19-GTGAAA    Cis200         T
58 GA–Index20-GTGGCC     OV167         U
59 GA–Index21-GTTTCG     OV167         C
60 GA–Index22-CGTACG     OV167         T

No special advice for you here. If the data says no significant differences, you may need to accept that as the conclusion.

As you said, the PCA plot is giving you a clue that treatment effect is not so large.

It would be better if you would include your code (generally always when posting to the support site). I'm assuming you are using ~sample + treatment as the design.