ALK Digital Pathology Outcome Prediction
Conditions: Alk-positive Non-Small Cell Lung Cancer, ALK-inhibitor Treated
Sponsor: Sheba Medical Center

                                                                                                                                     
 
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30th Sept 2024 
ALK Digital Pathology outcome prediction Study Protocol 
 
Multi-center 
Investigator-initiated 
Academic 
Retrospective study 
 
P.I.: Jair Bar, MD-PhD, Sheba Medical Center, Ramat Gan, Israel 
Sponsor: Institute of Oncology, Sheba MC, Ramat Gan, Israel 
 
 
 
 
 
Abbreviations 
 
ALK                Anaplastic lymphoma kinase  
BMI                Body Mass Index 
CI                   Confidence Interval 
CNN              Convolutional Neural Network CNN 
EMT               Epithelial-to-mesenchymal transition  
FISH              Fluorescence in situ hybridization 
ERK                 Extracellular signal-regulated kinase 
FFPE             Formalin-Fixed, Paraffin-Embedded 
H&E                Hematoxylin and Eosin  
IHC                   Immunohistochemistry 
mTOR            mammalian target of rapamycin 
MIL                multiple instance learning  
NGS              Next-generation sequencing 
NSCLC          Non-small-cell lung cancer 
NR                 non-responder 
SCLC             Small-cell lung cancer 
SMC              Sheba Medical Center  
VEGFR          Vascular endothelial growth factor receptor 
WSI                whole slide image  
 
 

                                                                                                                                     
 
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1. Background 
About 4% of NSCLC patients harbor a rearrangement of the ALK gene, producing a novel 
chimeric kinase that drives cancer evolvement and progression. ALK inhibitors have been 
developed and are currently the most active agents for the treatment of such patients, with a 
response rate around 80% (82.9% for alectinib-treated patients on the ALEX study1, 2. Regarding 
long-term outcome, on the ALEX trial at five-years follow up, the median survival was not reached 
yet with 62.5% of the alectinib-treated patients still alive 2. However, a subgroup of these patients 
do not respond to this treatment or demonstrate only a short-term response followed by rapid 
progression. Specifically, in the ALEX study, the one-year event-free survival was 68.4% (95% 
CI 61.0% to 75.9%), meaning 31.6% had progressed or died on alectinib within a year. In the 
crown study, 78% (95% confidence interval [CI], 70 to 84) were alive and without disease 
progression at 12 months in the lorlatinib group 3; thus, with this treatment only 22% had 
progressed or died within the first year. These results are numerically better with lorlatinib vs 
alectinib. However, the confidence intervals overlap and in general cross-trial comparisons are 
not valid. Therefore, and especially considering the very good long-term survival data for alectinib, 
lorlatinib is considered more relevant for later-line treatments.   
No tools are currently available to predict the response to ALK inhibitors and to identify the sub-
group of patients that will progress rapidly. Mechanisms of resistance are multiple, consisting of 
pathway-dependent ALK mutations4  or amplifications5, or off-target activation of bypass growth-
factor signaling pathways 6 . Additional mechanisms of resistance include epithelial-to-
mesenchymal transition (EMT) 7, SCLC transformation, activation of drug-efflux mechanisms; in 
a large proportion of cases, the mechanism of resistance remains elusive.  
Prediction of resistance is limited currently. Variant 3a is recognized as one with higher rate of 
resistance development, mostly by ALK mutations 8 . In general, resistance to advanced-
generation ALK inhibitors is more commonly accompanied by ALK mutations 4,6. Importantly, 
identification of ALK fusion by IHC may be a less reliable method than FISH or NGS.9  
Considering the poor outcome of some of ALK positive patients on ALK inhibitor treatment, it can 
be speculated that prediction of poor response to such treatment may allow personalized tailoring 
of more aggressive therapeutic options that are currently being tested in clinical trials. Such 
options include combination strategies with chemotherapy (e.g. study NCT05200481), 
combination with VEGF-signaling inhibition (e.g. NCT04227028) 10 , or with mTOR or ERK 
pathway inhibition11.  
Artificial intelligence-based methods of picture analysis have recently emerged as a novel way to 
characterize tumor specimens. The advent of digital pathology in combination with machine 
learning and other AI tools appears as the next revolution in the field of pathology12. AI tools are 
currently being tested for diagnosis and quantification of specific tumor characteristics such as 
IHC stains 13,14 or prediction of specific genetic aberrations15. Furthermore, AI allows integration 
of spatial information about location of specific cells as well as other features not captured by 
pathologist review. Recent studies including from our center16, demonstrated the feasibility of 
prediction of the response to immunotherapy based on machine learning of digital pathology H&E 
sections of melanoma 17 and lung cancer16,18. We aim to use this technique in order identify a 
sub-group of ALK positive patients that might be candidates for more aggressive treatment 
options. 
 
2. Objectives 
 
Hypothesis:  
Identification of ALK-positive patients’ subgroup at risk of rapid progression is possible at the time 
of diagnosis based on analysis of the H&E (Hematoxylin and Eosin) stained images of tumor 
sections.  
 
Goal of the study: to develop an AI-driven pathologic image analysis-based classifier that can 
identify patients unlikely to significantly benefit from the currently utilized first-line ALK inhibitors 
(advanced-generation ALK inhibitors). Our goal is a classifier with final ROC-AUC value of 0.75.   
 

                                                                                                                                     
 
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Endpoint of the study: the development of a classifier of outcome of ALK patients, to identify 
patients with a high likelihood of progression on 1st-line ALK inh (2nd or 3rd generation inhibitors) 
within a year. 
 
3. Design 
 
This is a retrospective study. 
 
All data have been collected at different time points during the patients’ routine visits at the 
hospital. 
1) 
Collection of a retrospective set of ALK positive patients with advanced NSCLC that have 
received an advanced-generation ALK inhibitor treatment as the first ALK inhibitor (i.e. 
alectinib, lorlatinib, brigatinib or ceritinib): collection of the clinical data, pathologic data, 
response to treatment and scans H&E images 
2) 
Image analysis of the scanned H&E images, development of a classifier of the data to 
identify responders vs. non-responders. 
 
 
4. Origin of the data/biological material 
 
Secondary use and analysis of data. 
 
Potential centers that will participate in the study include any Oncology center that can provide 
H&E images of diagnostic specimens of ALK+ NSCLC patients with clinical follow up of at least 
a year. It is expected that each center will be able to contribute 10-20 cases, thus to reach a 
sample size of 200, about 15 centers are required. 
 
Ethics: Each participating center is independent and will receive approval of the local IRB.  
 
Patient data confidentiality: No identifying information regarding participating patients will leave 
each of the centers. Participants will be given a study code, the key connecting the code to the 
patients’ identity will be kept at each participating site secured as per local regulations of each 
site.  
 
5. Study conduct 
 
1) Participating centers will secure an ethics and administrative approval for retrospective 
collection of clinical data, and provision of a single H&E stained FFPE section.  ALK 
positive patients will be identified from the working database of each center, data will be 
collected locally, deidentified and collected centrally. Data collected will include 
demographics (age, sex, smoking status, co-morbidities, BMI), tumor characteristics 
(histology, ALK testing utilized, specific test results, co-mutations if known, ALK fusion 
partner and variants, brain metastasis) and treatment (ALK inhibitor utilized, date initiated, 
dose reductions, treatment breaks, date of documented progression, date of death or last 
follow-up). We will aim to include only or mostly patients with NGS results available, to 
allow for robust and valid identification of the ALK-fusion, as well as the variant type and 
co-mutations, all proven potential confounders. Next, the required H&E slides will be 
stripped from identifying details, coded. The scanning of pathology slides will be 
performed; alternatively, the option of sending the image files instead of the slides 
themselves is available. 
2) Image analysis and AI development will be carried out at the Sheba Medical Center, in-
house development. The clinical data will be analyzed, tagging study samples as 
belonging to a responder (R), vs. a non-responder (NR). For the purpose of this study, a 
NR will be defined as a patient that has progressed or died on an ALK inhibitor treatment 
within the first year of treatment. Considering most patients would be on alectinib as first-
line ALK inhibitor, we expect about 32% of the samples to belong to the NR group. A 30-

                                                                                                                                     
 
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70 to 40-60 split between two classes is a reasonable split for the development of a 
classifier. 
 
The study cases will be randomly split to three: a training cohort, a validation cohort and 
a test cohort. The cohorts will be stratified by the response to treatment (i.e. equal 
proportion of R vs. NR cases in each cohort). Next, scanned images will be processed 
and analyzed. Slides analysis would be done using python using the pytorch packages. 
Further statistical analysis will be done with R statistical programming. 
At first the whole slide image (WSI) is divided into thousands of tiles. These are examined 
by a convolutional neural network (CNN) to extract tile level features. We will be using 
Resnet, a common deep learning model used for computer vision as the CNN.19The CNN 
will be trained with multiple instance learning (MIL) at the tile level and later the predicted 
scores will be aggregated for the WSI level20. The final model will be conducted on the 
slides, to distinguish between R vs. NR. The classifier will be developed on the training 
cohort, modified if required following processing of the validation cohort and finally tested 
for efficacy on the 
test cohort. Cross-
Validations 
techniques 
will 
also be used.  
 
 
 
 
 
 
 
 
6. Inclusion criteria 
 
 
Patient aged ≥ 18 years; 
 
Patient with an oncologic disease; 
 
ALK positive patients with advanced NSCLC that have received an advanced-generation 
ALK inhibitor treatment as the first ALK inhibitor (i.e. alectinib, lorlatinib, brigatinib or 
ceritinib). 
 
Clinical follow up of at least one year from initiation of ALK inhibitor (unless disease has 
progressed prior to this date).  
 
7. Exclusion criteria 
 Absence of information on the oncologic treatment received; 
 Lack of general or specific consent for this study  
 
 
8. Scientific methods and sample size 
 
The statistical method to define a required sample size for an AI-based algorithm development is 
not clearly defined at this point. Our target sample size (n=200) is based on several approaches 
of calculations. Firstly, based on our experience with similar type of studies. For example, an AI 
algorithm predicting of the response to immunotherapy based on digital pathology was developed 
based on a set of 76 cases in total 16. An alternative approach is based on our goal of a classifier 
with final ROC-AUC value of 0.75 or above with the null hypothesis value to be 0.5. We are aiming 
for a type 1 error value of 0.05 and for a power of 90%. Approximately 20% of patients are fast 
progressors (PD within first year of treatment). Total sample size required based on these 
assumptions is 96 with 16 fast progressors and 80 responders (calculated by MedCalc). 
Considering our goal to produce a highly robust classifier, and considering the relative lack of 
ALK WSI
Split to 
tiles
ResNet
Feature 
extraction
Grayscale
Background elimination
Etc.
ResNet
Prediction
MIL phase
%%%%%%
WSI prediction
Image 
extracted 
features

                                                                                                                                     
 
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robust manners to calculate sample size, we’ll aim for a sample size that is roughly 2-3 times 
larger than the mentioned numbers, and have set therefore our goal as n=200. 
AI-based analysis of images of the training cohort will be conducted using various methods, 
including machine-learning and deep learning methods. The methods deemed to provide optimal 
separation between R and NR will be utilized further and validated on the validation cohort and 
lastly on the test cohort. Training/validation/test sets will be derived from the entire study samples 
and split about 70:15:15.  
Classification of the cases to R and NR in each phase of the study, on each of the three cohorts 
(training, validation and test) will be examined for dependence on potential confounders by 
multivariate analysis. Covariates to be considered include: age, sex, BMI, smoking status, time 
from diagnosis of advanced disease to ALK inhibitor initiation, specific ALK inhibitor used, ALK-
fusion of variant 3 or other, co-mutations, histology, method of ALK-fusion analysis, presence of 
brain metastasis, site of tissue analysis and site of section scanning. Each of these additional 
clinical/molecular parameters will be utilized if available for more than 70% of the cohort. 
In addition, technical parameters such as precise fixation technique, width of sections, type of 
scanner and similar factors may have significant influence of the image analysis, and care will be 
taken not to integrate such factors into the algorithm. The generation of the classification algorithm 
will include at the learning phase repeated shuffling of the cases from various centers and different 
technical features, thus preventing biased classifications.  
 
 
 
 
9. References 
1 Peters S, Camidge DR, Shaw AT, et al. Alectinib versus Crizotinib in Untreated ALK-Positive Non-Small-
Cell Lung Cancer. N Engl J Med. 2017;377(9):829-838. doi:10.1056/NEJMoa1704795 
 
2 Mok T, Camidge DR, Gadgeel SM, et al. Updated overall survival and final progression-free survival data 
for patients with treatment-naive advanced ALK-positive non-small-cell lung cancer in the ALEX study. 
Annals of Oncology. Published online 2020. doi:10.1016/j.annonc.2020.04.478 
 
3 Shaw AT, Bauer TM, de Marinis F, et al. First-Line Lorlatinib or Crizotinib in Advanced ALK -Positive Lung 
Cancer . New England Journal of Medicine. 2020;383(21). doi:10.1056/nejmoa2027187 
 
4 Shaw AT, Solomon BJ, Besse B, et al. ALK resistance mutations and efficacy of lorlatinib in advanced 
anaplastic lymphoma kinase-positive non–small-cell lung cancer. Journal of Clinical Oncology. Published 
online 2019. doi:10.1200/JCO.18.02236 
 
5 Katayama R, Shaw AT, Khan TM, et al. Cancer: Mechanisms of acquired crizotinib resistance in ALK-
rearranged lung cancers. Sci Transl Med. 2012;4(120). doi:10.1126/scitranslmed.3003316 
 
6 Lin JJ, Riely GJ, Shaw AT. Targeting ALK: Precision medicine takes on drug resistance. Cancer Discov. 
2017;7(2). doi:10.1158/2159-8290.CD-16-1123 
 
7 Fukuda K, Takeuchi S, Arai S, et al. Epithelial-to-mesenchymal transition is a mechanism of ALK inhibitor 
resistance in lung cancer independent of ALK mutation status. Cancer Res. 2019;79(7). doi:10.1158/0008-
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8 Christopoulos P, Kirchner M, Endris V, Stenzinger A, Thomas M. EML4-ALK V3, treatment resistance, 
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9 Mok T, Peters S, Camidge DR, et al. Outcomes According to ALK Status Determined by Central 
Immunohistochemistry or Fluorescence In Situ Hybridization in Patients With ALK-Positive NSCLC Enrolled 
in the Phase 3 ALEX Study. Journal of Thoracic Oncology. 2021;16(2). doi:10.1016/j.jtho.2020.10.007 
 
                                                        

                                                                                                                                     
 
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