PowerPoint Presentation


The i31-gene expression profile test for cutaneous melanoma identifies patients with head and neck 
tumors who could forego sentinel lymph node biopsy

Background
›Up to 25% of patients with cutaneous melanoma (CM) have
tumors on the head or neck (HN).1 These patients have a worse
prognosis than those with tumors at other locations.2

›HN tumors present additional complexities for sentinel lymph
node biopsy (SLNB) due to complex lymphatic drainage systems,
technical challenges related to the location, and complex surgical
techniques required around cranial nerves and vasculature.3

›An algorithm integrating the 31-gene expression profile (31-GEP)
molecular risk stratification test with clinical and pathological
features (i31-GEP) provides a precise risk prediction for SLNB
positivity, which can help patients and clinicians make risk-
aligned decisions about undergoing SLNB among a high-risk
population.4 Separately, the i31-GEP has been validated to
predict risk-of-recurrence.

Acknowledgments & Disclosures
References

›TS and MT have no conflicts of interest. BM is an employee and shareholder/option holder of Castle
Biosciences, Inc.

Presented at the Winter Clinical 2023 Conference.

Teo Soleymani, MD1, Brian Martin, PhD2, Michael Tassavor, MD3

1Mohs Micrographic and Dermatologic Surgery, Cutaneous Oncology, Division of Dermatology, David Geffen School of Medicine, University of California, Los Angeles, CA, 2Castle Biosciences, Inc. Friendswood, TX, 3Skin Cancer Center, Cincinnati, OH

›Without sacrificing sensitivity, the i31-GEP for
SLNB could reduce the number of unnecessary
SLNBs by 37% overall and 63% for T1a tumors
and 50% for T1b tumors, specifically.
›No patients with an i31-GEP predicted SLN
positivity risk of <5% had a positive SLNB.
›The i31-GEP for SLNB can help guide risk-
aligned patient care decisions in patients
considering SLNB.

Conclusions

›Patients from a previously published multicenter cohort study4 with pre-
SLNB stage I tumors (T1-T2a) located in the HN region and who had
undergone SLNB were included in the analysis (n=159). Patients with
<5% and ≥5% risk predicted by the i31-GEP were considered low or
high-risk, respectively. A low-risk prediction was considered a negative
test result, and a high-risk prediction was considered a positive test result
for i31-GEP accuracy calculations.

Methods

1. Zito PM, et al: Melanoma Of The Head And Neck [Internet], in StatPearls. Treasure Island (FL), 2020. 2. Shashanka R, et al: Head and 

Neck Melanoma. ISRN Surg 2012:948302, 2012. 3. Giudice G, et al: Sentinel lymph node biopsy in head and neck melanoma. G Chir

35:149–55, 2014. 4. Whitman ED, et al: Integrating 31-Gene Expression Profiling With Clinicopathologic Features to Optimize 

Cutaneous Melanoma Sentinel Lymph Node Metastasis Prediction. JCO Precision Oncology 1466–1479, 2021

Figure 2.  The i31-GEP for SLNB can reduce SLNB rate 
while increasing the positivity rate 

i31-GEP for SLNB Percent

Sensitivity 100%
Specificity 39.1%

Negative predictive value 100%

Results
Table 1. The i31-GEP for SLNB has high sensitivity to identify 
patients likely to have SLN metastasis

Scan or click here  for more info

Figure 1. SLNB reduction rates in each T-category when using 
the i31-GEP for SLNB to guide decisions

No patient (0/59) with an i31-GEP predicted SLN positivity 
risk of <5% had a positive SLNB.

Using the i31-GEP for SLNB to guide SLNB decisions for 
T1-T2a tumors on the H&N achieved a 37% (59/159) SLNB 

reduction rate and increased the positivity rate from 5% 
(8/159 using AJCC staging) to 8% (8/100 using i31-GEP 

risk prediction).

Sensitivity: true positive/(true positive + false negative); specificity: true 
negative/(true negative + false positive); negative predictive value: true 
negative/(true negative + false negative)

H&N tumor, 
Received SLNB 

(T1a HR-T2a)

Perform 
SLNB

SLN –
95.0% 

(151/159)

SLN+
5% (8/159)

i31-GEP for 
SLNB

Low risk of 
SLN+ (<5%)

High risk of 
SLN+ (>10%)

Perform 
SLNB

SLN+ 8% 
(8/100)

Could have 
avoided 

SLNB

SLN- 100% 
(59/59)

AJCC staging

›Using the i31-GEP to identify patients for
SLNB can:
›Personalize melanoma clinical 
management plans
›Reduce the number of unnecessary 
SLNBs
›Reduce SLNB-associated complications
›Reduce healthcare costs

Clinical Impact

https://castlebiosciences.com/research-development/publications/

	Slide 1