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University of Milan
Giuseppe PelosiUniversity of Milan, IRCCS MultiMedica, Milan, Italy
Pathological grading of malignant pleural
mesothelioma
Pleural tumors: biomarkers and new entities
University of Milan
DISCLOSURE
NO CONFLICT OF
INTEREST
Pleural tumors: biomarkers and new entities
University of Milan
What is grading
• To unravel the biological aggressiveness• invasive and non-invasive lesions (LG/IG/HG)
• grading differentiation (that is only a part)SCLC = HG LG Polyps IG IPMN
• Diff. & independent of staging (extensive)• close relationship, but not a conceptual overlap
• Intensive property of tumors• biological recruitment to grow and spread
(“temperature” of tumor cells)
Heat capacity = mass
Average status of
molecular agitation
MPMMesothelial cells
Pass JTO 2016
Low G/High S
High G/Low S
MPM
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• Epithelioid MPM– 60-80%
• Biphasic MPM– 15-20%
• Sarcomatoid MPM
– 8-10%
– desmoplastic 2%2015
WHO classification of MPMEpidemiology
Clinics
Molecular
E
B
S
▪ Prognosis▪ to stratify different risk subsets
▪ Diagnosis▪ to classify tumors based on behavior
▪ Prediction▪ to treat individual patients at the best
Resection
specimens
Biopsy
samples
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Clinical handling of MPM: prognostic scores
• PGS is from clinical
prognostic scores• age, sex, TNM, laboratory
findings, performance status,
curative intent, BMI…
• PGS should be applicable to
every type of samples• resection and biopsy samples
• Simple, reproducible and
clinically useful
• PGS should offer timely
prognostic information• individual patient
• independent of therapy, type of
material and histology
• PGS should assist clinicians in• assigning different outcomes
• planning treatments
• strategy of FU
• clinical trial design
• The right drug, to the right
patient, at the right time
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Reasons for grading MPM
• A constellation of factors are
accounted for• age, TNM, laboratory findings, PS,
subtyping…
• 15-20% MPM (EMPM) > 3 years
• …but natural history is
largely unknown
• Case-mix of diversely behaving
(E>B>S) in earlier-stage tumors
Galateau-Salle, JTO 2018
• PGS is not managerially used in
clinical practice
Epithelioid MPMBiphasic MPMClinical handling of MPM patients
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Gx Grade of differentiation
cannot be assessed
G1 Well differentiated
G2 Moderately differentiated
G3 Poorly differentiated
G4 Undifferentiated
• Just histology (E, B, S)?
• Which other elements to account for? Cell atypia ?
Mitotic count ?
Necrosis ?
Growth pattern ?
• How to evaluate in different subtypes?
• Resection specimens or even biopsies?
PGS has not
been
established for
MPM, but
preliminary
data suggest
nuclear grade
(nuclear atypia
& mitoses) is
an independent
poor
prognosis
factor
Grading of MPM
2015
AJCC/UICC ?
University of Milan
A) NUCLEAR ATYPIA
1 mild
2 moderate
3 severe
B) MITOSES (10/HPF)
1 0-1
2 2-4
3 >5
NUCLEAR GRADE
A + B (2 to 6): I, II, III
Mono-Institutional
Resection specimens
Biopsy samples <5%
Only EMPM: 232 cases
Grading of MPM: just to start
Grade I
Grade II
Grade III
Nuclear grade
by scoring
score 2
score 3
score 4
score 5score 6
Nuclear grade
by category
I
IIIII
I
II
III
University of Milan
Grading of MPM: combination of parameters
Multi-Institutional
Resection specimens: 68%
Biopsy samples: 32%
Only EMPM: 776 cases
Grade I Grade II
Grade III
KADOTA’s
NUCLEAR GRADE
I score 2-3
II score 4-5
III score 6
NECROSIS
0 absent
1 present
MITOSIS-NECROSIS
SCORE
< 5 mitoses = 0
≥ 5 mitoses = 1
Score 0-2
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Grade I with
many mitoses
Grade I with
necrosisp<0.0001
Grading of MPM: combination of parameters
University of Milan
Grading of MPM: nuclear grade (b+c)
Grade I with
many mitoses
Grade I with
necrosis
0-1
2-4
>5
score 2-3
score 4-5
score 6
mild
Interm.
severe
University of Milan
Grading of MPM: necrosis and % solid
Grade I with
many mitoses
Grade I with
necrosis
University of Milan
Grade I with
many mitoses
Grade I with
necrosis
Grade Iscore 2-3Grade IIscore 4-5Grade IIIscore 6
NecrosisNo = 0Yes = 1
Greater separation of OS Alternative system(but not as robust as grade + necrosis)
NG + necrosis mitoses + necrosis
Grading of MPM: combination of parameters
University of Milan
VALIDATION SET612 CASES
PADOVA, MILANO, TORINO, MODENA,
GOLNIKTRAINING SET
328 CASES
BARI
• Multi-Institutional International Study
• Mostly biopsy samples
• 940 MPM (the largest series)
1) GRADING MODEL
MONO-INSTITUTIONAL
2) REPRODUCIBILITY’
(INTER-CENTERS AND INTER-OBSERVERS)
3) VALIDATION OF PGS
MULTI-INSTITUTIONAL
Pathologic Grading System
Necrosis (present v. absent)
Subtyping (E, B, S)
Mitotic count (1 mm2)
Ki67 (1 mm2 or 2000 cells)
Nuclear atypia (Kadota, Rosen)
Nucleoli (Fuhrman)
Cohen statistic for
reproducibility
k > 0.75 (CI 95%)
Grading of MPM: combination of parameters
University of Milan
HR < 2 = score 1
2 < HR < 4 = score 2
HR > 4 = score 4
Grading of MPM: combination of parameters
PGS outperformed mitoses and Ki-67 in the 12-month mortality prediction
(i.e., 76% of mortality was predicted by PGS at 12 months)
PGS maintained its prognostic power for mortality over time around 80%
(i.e., 80% of mortality was predicted by PGS over time)
45% mortality
increase
by 1 point score
31% mortality
increase
by 1 point score
University of Milan
• Individual patient
prediction
• Independent of
histology
• Reproducible
• Applicable to daily
routine and biopsies
• Potentially useful for
therapy planning
EMPM
BMPM
SMPM
Grading of MPM: combination of parameters
University of Milan
Grading of MPM
Sensitivity to chemotherapy
not only depended on
histologic subtyping, but
also nuclear grade/PGS
In MPM cell lines and healthy
donor PBMC, the expression of
immune checkpoints (PD-1, LAG-
3, TIM-3) and their ligands (PD-
L1, PD-L2, galectin-9) was
downregulated or unaltered by CT
agents (cisplatin) or immunogenic
agents (oxaliplatin)
First immune checkpoint blocking,
then chemotherapy
University of Milan
Grading of MPM
• All PGS proposals
need to be
prospectively tested
through clinical trialsKadota, Rosen Pelosi
• Is there something
else in the future?
University of Milan
A machine learning approach
❖ Clinical data or OMIC results
❖ Pathology is yet missing❖ subtyping of MPM
❖ …no big data by mining on
histologic sections
University of Milan
➢ University of Milan, Italy
➢ Polytechnic of Milan, Italy
➢ European Institute of Oncology, Milan, Italy
➢ University of Bari, Italy
➢ University of Florence, Italy
➢ University of Insubria, Varese, Italy
➢ University of Padua, Italy
➢ University of Pisa, Italy
➢ University of Turin, Italy
➢ ULB University, Brussels, Belgium
➢ Sophia Antipolis University, Nice, France
➢ University Clinic Golnik, Slovenia
➢ University of Coimbra, Portugal
IMMINENT STUDY
IMage MinIng for luNg
nEuroendocriNe Tumors
➢ 1000 lung NENs
➢ Machine learning analysis
A machine learning approach
University of Milan
• Image analysis of
620 parameters
- spatial statistics
- graph modeling
- fractality
- Shannon entropy
Machine learning on 30 lung NENs
Intratumor
heterogeneity
of Ki-67
Kurtosis of closeness centrality
Second quartile of transmission
Grade kurtosis
Second quartile of closeness
centrality
Mean of L-function
Support Vector Machines with polynomial kernel
5 parameters
• 5-fold cross
validation
approach for
each image,
repeated 100
times
A B C D E
Training
Prediction
A B C D EA B C D EA B C D EA B C D E
➢ Sensitivity = 73.6% (dead patients)
➢ Specificity = 89.6% (alive patients)
➢ Accuracy of prediction = 84.3%
➢ PPV = 78.2% (dead)
➢ NPN = 87.4% (alive)
Pelosi et al 2019 (manuscript in preparation)
University of Milan
❑ We cannot yet disregard morphology while “grading” MPM
(E, B, S), but an integrated multi-scoring PGS should be
implemented
❑ Machine learning approach could help in the clinical
outcome prediction at the level of individual patients
❑ Clinical trials on MPM patients should account for PGS in
the accrual phase to highlight a clinical role
Take home messages
Thank you
