Back

Synthetic lethality-based prediction of cancer treatment response from histopathology images

Hoang, D.-T.; Dinstag, G.; Hermida, L. C.; Ben-Zvi, D. S.; Elis, E.; Caley, K.; Sinha, S.; Sinha, N.; Dampier, C. H.; Beker, T.; Aldape, K.; Aharonov, R.; Stone, E. A.; Ruppin, E.

2022-06-09 cancer biology
10.1101/2022.06.07.495219 bioRxiv
Show abstract

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Importantly, ENLIGHT-DeepPT successfully predicts true responders in five independent patients cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, trained and tested on the same cohort in cross validation. Its future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries. Statement of SignificanceENLIGHT-DeepPT is the first approach shown to successfully predict response to multiple targeted and immune cancer therapies from H&E slides. In distinction from all previous H&E slides prediction approaches, it does not require supervised training on a specific cohort for each drug/indication treatment but is trained to predict expression on the TCGA cohort and then can predict response to an array of treatments without any further training. ENLIGHT-DeepPT can provide rapid treatment recommendations to oncologists and help advance precision oncology in underserved regions and low-income countries.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
npj Precision Oncology
48 papers in training set
Top 0.1%
14.1%
2
PLOS Computational Biology
1633 papers in training set
Top 5%
6.7%
3
JCO Clinical Cancer Informatics
18 papers in training set
Top 0.1%
6.7%
4
Nature Communications
4913 papers in training set
Top 33%
4.8%
5
PLOS ONE
4510 papers in training set
Top 34%
4.2%
6
Scientific Reports
3102 papers in training set
Top 32%
3.9%
7
Clinical Cancer Research
58 papers in training set
Top 0.4%
3.9%
8
Genome Medicine
154 papers in training set
Top 2%
3.5%
9
Nature Medicine
117 papers in training set
Top 0.9%
3.5%
50% of probability mass above
10
Cancer Research
116 papers in training set
Top 1%
3.2%
11
Cell Reports Medicine
140 papers in training set
Top 2%
3.2%
12
Frontiers in Oncology
95 papers in training set
Top 2%
2.3%
13
Journal of Translational Medicine
46 papers in training set
Top 0.5%
2.1%
14
npj Digital Medicine
97 papers in training set
Top 2%
1.9%
15
Cancers
200 papers in training set
Top 3%
1.7%
16
Journal for ImmunoTherapy of Cancer
64 papers in training set
Top 0.6%
1.7%
17
British Journal of Cancer
42 papers in training set
Top 0.9%
1.7%
18
Bioinformatics Advances
184 papers in training set
Top 3%
1.6%
19
Nature Machine Intelligence
61 papers in training set
Top 2%
1.3%
20
Cancer Medicine
24 papers in training set
Top 0.9%
1.3%
21
BMC Cancer
52 papers in training set
Top 2%
1.2%
22
Nature Cancer
35 papers in training set
Top 1%
1.1%
23
Modern Pathology
21 papers in training set
Top 0.3%
1.1%
24
Cancer Research Communications
46 papers in training set
Top 0.8%
0.9%
25
European Journal of Cancer
10 papers in training set
Top 0.4%
0.9%
26
npj Systems Biology and Applications
99 papers in training set
Top 2%
0.8%
27
iScience
1063 papers in training set
Top 30%
0.8%
28
Breast Cancer Research
32 papers in training set
Top 0.5%
0.7%
29
Bioinformatics
1061 papers in training set
Top 10%
0.7%
30
Frontiers in Genetics
197 papers in training set
Top 11%
0.7%