1. The association of NFRP classes with event-free survival (EFS) is of borderline significance in our T-ALL patients
A total of 86 newly diagnosed adult T-cell acute lymphoblastic leukemia (T-ALL) were included in the present study (Table 1). For 54 of these patients, RNA-seq data were available from our previous work [7]. The present study included an additional 32 patients, which without RNA-seq data but with detailed clinical information.
Table 1
Clinical characteristics of our training (RNA-seq data, n= 54) and test (RT-qPCR data, n= 32) cohorts of adult T-ALL patients
Characteristics
|
Number of patients
|
Association with EFS
|
Datasets
|
HR (95%CI)
|
p value*
|
Training
|
Test
|
p value#
|
Total
|
86
|
|
|
54
|
32
|
|
Gender
|
|
1.339(0.675-2.653)
|
0.404
|
|
|
|
male
|
58
|
|
|
36
|
22
|
0.842
|
female
|
28
|
|
|
18
|
10
|
Age (years)
|
|
1.391(0.683-2.832)
|
0.363
|
|
|
|
<35
|
53
|
|
|
32
|
21
|
0.557
|
≥35
|
33
|
|
|
22
|
11
|
WBC, ×109/L
|
|
2.814(1.219-6.495)
|
0.015
|
|
|
|
≤100
|
68
|
|
|
41
|
27
|
0.352
|
>100
|
18
|
|
|
13
|
5
|
Immunophenotype
|
|
1.455(0.678-3.124)
|
0.336
|
|
|
|
ETP
|
27
|
|
|
14
|
13
|
0.156
|
non-ETP
|
59
|
|
|
40
|
19
|
Karyotype
|
|
1.0134(0.579-2.221)
|
0.714
|
|
|
|
Normal
|
43
|
|
|
25
|
18
|
0.401
|
Abnormal
|
31
|
|
|
21
|
10
|
CR (%)
|
61 (70.9%)
|
|
|
41
|
20
|
0.185
|
MRD post induction
|
|
3.389(1.683-6.824)
|
0.001
|
|
|
|
≥10^4
|
45
|
|
|
29
|
16
|
0.898
|
<10^4
|
38
|
|
|
25
|
13
|
Median follow-up time (months) (95% CI)
|
55
(46.4-63.6)
|
/
|
/
|
79
(46.3-111.7)
|
25
(46.4-63.6)
|
/
|
EFS, event-free survival; WBC, white blood cells counts; ETP, early T cell precursors; CR, complete remission; MRD, minimal residual disease; HR, hazard ratio; *, p-value determined by the log-rank test; #, p-value determined by using two-tailed χ2 test.
|
The N/F mutational status as well as the N/F combined with RAS and PTEN (NFRP) mutation status were reported to impact adult T-ALL patients [11–14]. Therefore, NOTCH1, FBXW7, RAS and PTEN mutation status were also assessed for all our T-ALL adult patients. Clinical and biological features of patients with T-ALL were analyzed according to the mutational status of N/F or NOTCH1/FBXW7/RAS/PTEN (NFRP classes) as summarized in Supplementary Table 1. NFRP classes were defined as follows: patients with N/F mutation but without RAS or PTEN mutations were assigned to class I, and the patients with other mutational status were assigned to class II as defined by Trinquand et al [12]. There was no significant association between oncogenetic classifiers and clinical features. Noticeably, early T-cell precursor (ETP) ALL was more frequently observed in NFRP class II than in NFRP class I (45.5% versus 22.6%; p = 0.027).
We then analyzed the impact of N/F mutational status and NFRP classes on patient survival probabilities considering overall survival (OS) and event-free survival (EFS). N/F mutated patients showed increased OS and EFS, although for OS it was of borderline significance (with log-rank p = 0.049 for OS and p = 0.01 for EFS) (Supplementary Fig. 1A). Consistent with Trinquand and colleagues [12], prognostic prediction ability of NFRP classes was improved compared to the classification based only on the N/F mutational status. Indeed, NFRP class II patients predicted significantly shorter OS and EFS than those of NFRP class I (log-rank p = 0.037 for OS and p = 0.009 for EFS) (Supplementary Fig. 1B. However, the NFRP classifier only remained a significant prognostic covariate for EFS when adjusting to age (using the 35-year cutoff) and WBC count (using the 100×10^9/L cutoff) (EFS: HR = 1.751; 95% CI, 1.011 to 3.03; p = 0.045; and OS: HR = 1.623; 95% CI, 0.917 to 2.873; p = 0.097).
2. A combination of ectopically expressed genes can be used to reliably predict prognosis of T-ALL patients at diagnosis
These observations prompted us to seek for new biomarkers which could reliably stratify patients before treatment.
We applied a strategy specifically designed to identify the aberrant expression of genes which are normally silent in non-germline adult tissues and to test the association of these ectopic expressions with survival probabilities.
By using available RNA-seq data in large series of normal human tissues, we identified 3195 transcripts with an expression restricted to testis, placenta or embryonic stem cells, of which 448 were found ectopically expressed in at least 10% and not in more than 90% T-ALLs samples. We then used a first cohort of T-ALL patients for whom RNA-seq as well as survival data were available. In addition to the 54 T-ALL adult patients, in order to strengthen the power of the approach, RNA-seq data obtained from 55 samples of children with T-ALL were also included in the training cohort. Considering each of the 448 genes ectopically expressed in a subgroup of T-ALL, we compared survival probabilities of the two groups of patients, whose malignant cells respectively did or did not express the gene. A total of 18 different genes (Supplementary Table 2) were identified whose activation was significantly associated with OS and/or EFS in our T-ALL series.
In order to assess the value of combinations of these genes in terms of prognostic biomarkers, we then tested all possible combinations of the 18 genes for their potentiality to stratify T-ALL patients. Among them, the 5-gene set of ZPBP, GOT1L1, ACTRT2, SPATA45 and TOPAZ1 (all restricted to male germ cells) was identified as an optimal classifier for prognostic stratification in T-ALL patients (p < 10-4 for OS and p < 10-5 for EFS). All T-ALL patients were then assigned to 2 groups according to the ectopic activation of the 5 genes (Fig. 1A). Those expressing at least one of the 5 genes were assigned to the “5-gene expression classifier” (5-GEC) positive group. The other patients, expressing none of the five genes were assigned to the 5-GEC negative group. As illustrated by Kaplan-Meier plots, this classification system can well separate patients into different risk groups considering all T-ALL cases, or subsets of either children or adult T-ALL patients (Supplementary Fig. 2). In particular, 5-GEC positive and negative T-ALL adult patients showed significant differences in terms of survival probabilities (log-rank p = 0.01 for OS and p = 0.004 for EFS (Fig. 1A)).
In order to validate the predictability of the 5-GEC, we detected the expression of the 5 genes in a second cohort, the test cohort of 32 T-ALL adult patients by using RT-qPCR. As a result, out of the 32 cases, 6 patients were assigned to the 5-GEC negative group, whereas the other 26 patients were 5-GEC positive. Kaplan-Meier plots also demonstrated significant differences in both OS and EFS (Log-rank test p = 0.029 and p = 0.032 respectively, Fig. 1B).
3. A stratification based on 5-GEC predicts MRD status and identifies MRD negative patients with high risk of relapse
MRD status following induction therapy in patients with ALL has been routinely used to predict outcome, and has been reported to strongly and consistently associate with clinical outcomes in ALL [4]. Consistently, positive MRD was predictive of significantly inferior OS and EFS in our cohort (p < 0.001 for both OS and EFS, Fig. 2A). However, MRD status is not available at the time of diagnosis. Additionally, recurrence of the disease also occur in patients with negative MRD decreasing the probability of overall and event-free survival [1]. Interestingly, our newly identified 5-GEC classifier turned out to be a very efficient predictor of MRD positivity (Fig. 2B, chi-square test, p < 0.001). Moreover, within the MRD negative subgroup, 5-GEC positivity was associated with shorter survival with high significance (p = 0.036 for EFS, Fig. 2C), thus differentiating patients who are likely to respond well to standard therapy from those who may benefit from more intensive therapy. This observation was further confirmed in our test cohort (Supplementary Fig. 3).
4. Gene expression profile of 5-GEC positive T-ALL is significantly depleted in genes involved in basic cellular activities and identifies specific characteristics in MRD negative / 5-GEC positive T-ALL
In order to further characterize the molecular profile of 5-GEC positive aggressive T-ALL we performed Gene Set Enrichment Analysis (GSEA) to highlight biological pathways correlating with that of 5-GEC positive versus negative T-ALL adult samples. GSEA was performed either considering the whole population of our T-ALL patients with available transcriptomic data or focusing on MRD negative T-ALL only.
Interestingly, the GSEA profiles of these aggressive forms of T-ALL revealed a major down-regulation of most cellular activities. Gene sets constituted of genes involved in cell proliferation and mitosis, or RNA ribosomal and translation activities, as well as mitochondria and related metabolic activities, were among the most significantly downregulated in 5-GEC positive T-ALL (Fig. 3A), suggesting that these aggressive T-ALL forms were those enriched in “dormant” cells. Remarkably, the 5-GEC positive T-ALL cells are not expressing many of the genes normally expressed in hematopoietic stem cells (Fig. 3B).
Interestingly, part of the transcriptomic profile of 5-GEC positive T-ALL is also shared with MRD positive T-ALL. Indeed, 5-GEC positive ALL and MRD positive ALL were both depleted for gene sets representative of genes involved in the mitotic cell cycle, E2F and MYC targets, RNA processing, ribosome biogenesis and translational processes, as well as oxidative phosphorylation and mitochondria related functions (Fig. S4).
However, genes differentially expressed between 5-GEC positive and negative adult T-ALL samples only partially overlap with those differentially expressed between the MRD positive or negative subgroups. As illustrated in Fig. 4, the heatmaps illustrate the expression of the genes down- and up-regulated in 5-GEC positive versus negative patients T-ALL patients with an absolute fold change of expression values between 5-GEC positive and negative patients above 1.5 and a t-test p-value<0.05. Respectively 339 and 302 genes were down and up regulated considering all adult T-ALL patients (Fig. 4A), and respectively 326 and 187 genes were down and up regulated considering only MRD negative adult T-ALL patients (Fig. 4B). Moreover, the gene expression signature of 5-GEC positive versus negative patients considering all T-ALL adult patients (n=54) and the gene expression signature of MRD positive versus negative patients are not correlated (Spearman coefficient = 0.20), while the gene expression signatures of 5-GEC positive versus negative patients considering all T-ALL adult patients (n=54) or considering T-ALL patients with MRD negative status only (n=25) are highly correlated (Spearman coefficient = 0.81). This suggests that 5-GEC positive T-ALL had specific characteristics that may explain why some of them which were detected as MRD negative were actually still prone to relapse. Indeed, several path-ways and functions are specifically associated with the 5-GEC signature and not shared by MRD positive ALL.
The GSEA signature of 5-GEC positive ALL within the MRD negative group well illustrates this specificity (Fig. 5). One specific feature of the 5-GEC positive ALL signature is that it is highly enriched in mRNAs from genes encoding histones and chromatin proteins as opposed to MRD positive ALL, which showed a depletion for these same mRNAs (Fig. 5: 1st and 2nd rows). Another striking characteristic of these 5-GEC positive ALL is the complete shutdown of mitochondria-encoded transcripts. Indeed, mitochondria related genes are globally depleted in both MRD positive and 5-GEC positive cells, but the expression of the 13 genes located on the mitochondria genome remain high in MRD positive ALL (as compared to MRD negative). In 5-GEC positive ALL, the situation is different since these same 13 genes are completely shut down (Fig. 5: 4th row), suggesting that a dramatic impairment of mitochondria transcriptional activity is specifically associated with these 5-GEC positive ALL.