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Model Details

Model Description

This adapter is a LoRA fine-tuned module designed to enhance Named Entity Recognition (NER) capabilities on free-text NGS-LEVEL-2 reports.
It enables structured extraction of key entities related to genetic testing, such as gene names, mutations, results, and testing methods.
The adapter is specifically trained to work on NGS-LEVEL-2 records and must be used in conjunction with the Llama-3.1-8B base model.

Example

  • Input
์—ฐ๊ฒฐ๋ณ‘๋ฆฌ๋ฒˆํ˜ธ : S 230017454
์ฑ„์ทจ๋ถ€์œ„ : liver
์ˆ˜ ์ˆ  ๋ช… : USG liver bx
์ž„์ƒ์ง„๋‹จ : breast cancer r/o liver meta
_____________________________________________________________________________________________________________________________

NGS๊ธฐ๋ฐ˜ ์œ ์ „์žํŒจ๋„๊ฒ€์‚ฌ(๊ณ ํ˜•์•”, Level 2 + RNA Fusion):

RESULTS:  (์Šค์บ”์ด๋ฏธ์ง€ ์ฐธ์กฐ)

Sample Adequacy:  Adequate

์œ ๋ฐฉ์•” ๊ด€๋ จ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ
  ATM    ๋ฏธ๊ฒ€์ถœ   NF1     NF1 p.(Y2285Tfs*5) c.6852_6855delTTAC
  BRCA1  ๋ฏธ๊ฒ€์ถœ   PALB2   ๋ฏธ๊ฒ€์ถœ
  BRCA2  ๋ฏธ๊ฒ€์ถœ   PIK3CA  PIK3CA p.(H1047R) c.3140A>G
  CDH1   ๋ฏธ๊ฒ€์ถœ   PTEN    ๋ฏธ๊ฒ€์ถœ
  CHEK2  ๋ฏธ๊ฒ€์ถœ   STK11   ๋ฏธ๊ฒ€์ถœ
  ERBB2  ๋ฏธ๊ฒ€์ถœ   TP53    TP53 p.(L45Afs*7) c.132_133insG
  NBN    ๋ฏธ๊ฒ€์ถœ

VARIANTS FOUND
Tier 1 
  PIK3CA 
    DNA change:  c.3140A>G
    AA change:  p.(H1047R)
    Type:  missense
    Read depth (x):  2000
    VAF (%):  60.10 % 

Tier 2 
  NF1 
    DNA change:  c.6852_6855delTTAC
    AA change:  p.(Y2285Tfs*5)
    Type:  Frameshiftdeletion
    Read depth (x):  1978
    VAF (%):  71.28 %

  TP53 
    DNA change:  c.132_133insG
    AA change:  p.(L45Afs*7) 
    Type:  Frameshiftinsertion
    Read depth (x):  1984
    VAF (%):  67.79 %

  KEAP1 
    DNA change:  c.807_810delGGCC
    AA change:  p.(A270Cfs*6)
    Type:  Frameshiftdeletion
    Read depth (x):  1985
    VAF (%):  81.46 % 

  CCND1 amplification:  8.08 copies

  FGF19 amplification:  8.02 copies

  FGF3 amplification:  6.69 copies

  FGF4 amplification:  6.61 copies

  EMSY amplification:  5.4 copies

Tier 3
  MAP3K1 
    DNA change:  c.3011delA
    AA change:  p.(H1004Lfs*78) 
    Type:  Frameshiftdeleetion
    Read depth (x):  1137
    VAF (%):  62.71 %

  NCOR1 
    DNA change:  c.5006delG
    AA change:  p.(G1669Efs*93) 
    Type:  Frameshiftdeletion
    Read depth (x):  1176
    VAF (%):  72.87 %

๊ฒฐ๊ณผํ•ด์„
๊ฒ€์‚ฌ๊ฒฐ๊ณผ PIK3CA ์œ ์ „์ž์—์„œ p.(H1047R) ๋ฐœ๊ฒฌ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. PIK3CA ์œ ์ „์ž๋Š” ํด๋ž˜์Šค I ํฌ์ŠคํŒŒํ‹ฐ๋”œ์ด๋…ธ์‹œํ†จ 3-ํ‚ค๋‚˜์ œ(PI3K) ํšจ์†Œ์˜ ํฌ์ŠคํŒŒํ‹ฐ๋”œ์ด๋…ธ์‹œํ†จ-4,5-๋น„์Šคํฌ์ŠคํŽ˜์ดํŠธ 3-ํ‚ค๋‚˜์ œ ์ด‰๋งค ์„œ๋ธŒ์œ ๋‹› ์•ŒํŒŒ๋ฅผ ์ธ์ฝ”๋”ฉํ•ฉ๋‹ˆ๋‹ค. PI3K๋Š” 4๊ฐœ์˜ p110 ์ด‰๋งค ์„œ๋ธŒ์œ ๋‹› ์ค‘ ํ•˜๋‚˜๋ฅผ ํ™œ์„ฑํ™”๋œ ํ‹ฐ๋กœ์‹  ๋‹จ๋ฐฑ์งˆ ํ‚ค๋‚˜์ œ์— ์—ฐ๊ฒฐํ•˜๋Š” p85 ์กฐ์ ˆ ์„œ๋ธŒ์œ ๋‹›์„ ํฌํ•จํ•˜๋Š” ์ด์ข…์ด๋Ÿ‰์ฒด์ž…๋‹ˆ๋‹ค. p110 ์ด‰๋งค ์†Œ๋‹จ์œ„๋Š” p110ฮฑ, ฮฒ, ฮด, ฮณ๋ฅผ ํฌํ•จํ•˜๊ณ  ๊ฐ๊ฐ ์œ ์ „์ž PIK3CA, PIK3CB, PIK3CD ๋ฐ PIK3CG์— ์˜ํ•ด ์ธ์ฝ”๋”ฉ๋ฉ๋‹ˆ๋‹ค. PI3K๋Š” ํฌ์ŠคํŒŒํƒ€์ œ ๋ฐ ํ…์‹  ๋™์กฑ์ฒด (PTEN)์€ ์—ญ๋ฐ˜์‘์„ ์ด‰๋งคํ•ฉ๋‹ˆ๋‹ค. ์ด๋…ธ์‹œํ†จ ์ง€์งˆ์˜ ๊ฐ€์—ญ์  ์ธ์‚ฐํ™”๋Š” ์„ธํฌ ์„ฑ์žฅ ๋ฐ ๋Œ€์‚ฌ์˜ ๋‹ค์–‘ํ•œ ์ธก๋ฉด์„ ์กฐ์ ˆํ•ฉ๋‹ˆ๋‹ค. PIK3CA์˜ ๋ฐ˜๋ณต์ ์ธ ์ฒด์„ธํฌ ๋ณ€ํ˜•์€ ์•”์—์„œ ์ž์ฃผ ๋ฐœ์ƒํ•˜๋ฉฐ PI3K/AKT/MTOR ๊ฒฝ๋กœ์˜ ํ™œ์„ฑํ™”๋ฅผ ์ดˆ๋ž˜ํ•˜๋ฉฐ, ์ด๋Š” ์„ธํฌ ์ฆ์‹, ์„ธํฌ์ž๋ฉธ์‚ฌ, ์•”์„ธํฌ ๋Œ€์‚ฌ ๋ฐ ์นจ์Šต, ์œ ์ „์  ๋ถˆ์•ˆ์ •์„ฑ์„ ๋น„๋กฏํ•œ ์•”์˜ ์—ฌ๋Ÿฌ ํŠน์ง•์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. PIK3CA์˜ ์žฌ๋ฐœ์„ฑ ์ฒด์„ธํฌ ํ™œ์„ฑํ™” ๋Œ์—ฐ๋ณ€์ด๋Š” ๋‹ค์–‘ํ•œ ์•”์—์„œ ํ”ํ•˜๋ฉฐ ์œ ๋ฐฉ์•”, ์ž๊ถ๊ฒฝ๋ถ€์•”, ์ž๊ถ์•”์˜ 20-30%, ๋ฐฉ๊ด‘์•”, ์œ„์•”, ๋‘๊ฒฝ๋ถ€์•”, ๋Œ€์žฅ์•”์˜ 10-20%์—์„œ ๊ด€์ฐฐ๋ฉ๋‹ˆ๋‹ค. PIK3CA์˜ ํ™œ์„ฑํ™” ๋Œ์—ฐ๋ณ€์ด๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ๊ฐ๊ฐ ๊ณ ์œ ํ•œ ํ™œ์„ฑํ™” ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ฐ–๋Š” ์—‘์† 9 ๋‚˜์„ (์ฝ”๋ˆ E542/E545) ๋ฐ ์—‘์† 20 ํ‚ค๋‚˜์ œ(์ฝ”๋ˆ H1047) ๋„๋ฉ”์ธ์— ํ•ด๋‹นํ•˜๋Š” ๋‘ ์˜์—ญ์— ํด๋Ÿฌ์Šคํ„ฐ๋ง๋ฉ๋‹ˆ๋‹ค. PIK3CA๋Š” ํ, ์ž๊ถ๊ฒฝ๋ถ€, ๋‘๊ฒฝ๋ถ€, ์‹๋„์˜ ํŽธํ‰์ƒํ”ผ์•”, ์žฅ์•ก์„ฑ ๋‚œ์†Œ์•” ๋ฐ ์ž๊ถ์•”์„ ๋น„๋กฏํ•œ ๋‹ค์–‘ํ•œ ์•”์—์„œ ์ž์ฃผ ์ฆํญ๋˜๋Š”(10-30%) ์˜์—ญ์ธ 3q26 ์„ธํฌ๋Œ€์— ์žˆ์Šต๋‹ˆ๋‹ค. PI3K ์–ต์ œ์ œ์ธ ์•ŒํŽ ๋ฆฌ์‹ญ์€ PIK3CA ๋Œ์—ฐ๋ณ€์ด, ํ˜ธ๋ฅด๋ชฌ ์ˆ˜์šฉ์ฒด(HR) ์–‘์„ฑ, ์ธ๊ฐ„ ํ‘œํ”ผ ์„ฑ์žฅ ์ธ์ž ์ˆ˜์šฉ์ฒด 2(HER2) ์Œ์„ฑ, ์ง„ํ–‰์„ฑ ๋˜๋Š” ์ „์ด์„ฑ ์œ ๋ฐฉ ํ™˜์ž์˜ ์น˜๋ฃŒ๋ฅผ ์œ„ํ•ด ํ’€๋ฒ ์ŠคํŠธ๋ž€ํŠธ์™€ ๋ณ‘์šฉํ•˜์—ฌ FDA ์Šน์ธ(2019)๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ, ์ „์ด์„ฑ ์—์ŠคํŠธ๋กœ๊ฒ ์ˆ˜์šฉ์ฒด(ER) ์–‘์„ฑ ์œ ๋ฐฉ์•” ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์•ŒํŽ ๋ฆฌ์‹ญ๊ณผ ๋ ˆํŠธ๋กœ์กธ์˜ 1b์ƒ ์—ฐ๊ตฌ์—์„œ 6๊ฐœ์›” ์ด์ƒ ์งˆ๋ณ‘ ์ง„ํ–‰์˜ ๊ฒฐ์—ฌ๋กœ ์ •์˜๋˜๋Š” ์ž„์ƒ ์ด์ต๋ฅ ์€ PIK3CA์—์„œ 44%(7/16)์˜€์œผ๋ฉฐ, ๋Œ์—ฐ๋ณ€์ด๋œ ์ข…์–‘ ๋ฐ PIK3CA ์•ผ์ƒํ˜• ์ข…์–‘์—์„œ 20%(2/20)์˜ ์ž„์ƒ์ด์ต๋ฅ ์„ ๋‚˜ํƒ€๋‚ด์—ˆ์Šต๋‹ˆ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ์—‘์† 20 H1047R ๋Œ์—ฐ๋ณ€์ด๋Š” ์—‘์† 9 E545K ๋Œ์—ฐ๋ณ€์ด์™€ ๋น„๊ตํ•˜์—ฌ ๋” ์ง€์†์ ์ธ ์ž„์ƒ ๋ฐ˜์‘๊ณผ ์—ฐ๊ด€๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์•ŒํŽ ๋ฆฌ์‹ญ์€ PIK3CA ๋Œ์—ฐ๋ณ€์ด๊ฐ€ ์žˆ๋Š” ER+ ์ดˆ๊ธฐ ์œ ๋ฐฉ์•” ํ™˜์ž์—๊ฒŒ ๋ ˆํŠธ๋กœ์กธ๊ณผ ํ•จ๊ป˜ ํˆฌ์—ฌํ–ˆ์„ ๋•Œ ๋ฐ˜์‘์„ ๊ฐœ์„ ํ•˜์ง€ ๋ชปํ–ˆ์Šต๋‹ˆ๋‹ค. MTOR ์–ต์ œ์ œ ์‹œ๋กค๋ฆฌ๋ฌด์Šค ๋ฐ ํ…œ์‹œ๋กค๋ฆฌ๋ฌด์Šค๋ฅผ ์‚ฌ์šฉํ•œ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋Š” PIK3CA ๋Œ์—ฐ๋ณ€์ด ๋‚œ์น˜์„ฑ ์•”์—์„œ ์ž„์ƒ ๋ฐ˜์‘์˜ ๊ฐœ๋ณ„ ์‚ฌ๋ก€๋ฅผ ๋ณด๊ณ ํ•ฉ๋‹ˆ๋‹ค.

๊ฒ€์‚ฌ๊ฒฐ๊ณผ NF1 ์œ ์ „์ž์—์„œ p.(Y2285Tfs*5) ๋ฐœ๊ฒฌ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. NF1 ์œ ์ „์ž๋Š” Ras-GTPase-activating protein (GAP) ๊ณ„์—ด ๋‚ด์˜ ์ข…์–‘ ์–ต์ œ์ œ ์ธ neurofibromin ๋‹จ๋ฐฑ์งˆ์„ ์•”ํ˜ธํ™”ํ•ฉ๋‹ˆ๋‹ค. NF1์€ ํ™œ์„ฑ GTP ๊ฒฐํ•ฉ ์ƒํƒœ๋ฅผ ๋น„ํ™œ์„ฑ GDP ๊ฒฐํ•ฉ ์ƒํƒœ๋กœ ํ•˜ํ–ฅ ์กฐ์ ˆํ•จ์œผ๋กœ์จ KRAS, NRAS ๋ฐ HRAS๋ฅผ ํฌํ•จํ•œ ํ™œ์„ฑํ™” ๋œ RAS ๋‹จ๋ฐฑ์งˆ์„ ์กฐ์ ˆํ•ฉ๋‹ˆ๋‹ค. Missense mutation์œผ๋กœ ์ธํ•œ NF1์˜ ๋น„ํ™œ์„ฑํ™”๋Š” RAS-GTP์˜ ์„ธํฌ ๋‚ด ์ˆ˜์ค€์„ ์œ ์ง€ํ•˜๊ณ  RAS / RAF / MAPK ๋ฐ PI3K / AKT / mTOR ์‹ ํ˜ธ ์ „๋‹ฌ ๊ฒฝ๋กœ์˜ ํ™œ์„ฑํ™”๋ฅผ ์—ฐ์žฅํ•˜์—ฌ ์„ธํฌ์ฆ์‹์„ ์œ ๋„ํ•˜๊ณ  ๋ฐ ์„ธํฌ์˜ ์ƒ์กด์„ ์ฆ๊ฐ€์‹œํ‚ต๋‹ˆ๋‹ค. NF1์˜  constitutional mutation์€  juvenile myelomonocytic leukemia (JMML)๊ณผ myeloproliferative neoplasms (MPN)๊ณผ ๊ฐ™์€ ๊ณจ์ˆ˜์„ฑ ์•…์„ฑ ์ข…์–‘์— ๋Œ€ํ•œ ์†Œ์ธ์ด์žˆ๋Š” neurofibromatosis type 1๊ณผ ๊ด€๋ จ์ด ์žˆ์Šต๋‹ˆ๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ NF1 ๋Œ์—ฐ๋ณ€์ด ์ข…์–‘์€ NF1์˜ biallelic inactivation์„ ๋‚˜ํƒ€๋‚ด๋ฉฐ ๋ฐœ์•”์— ๋Œ€ํ•œ 'two-hit' hypothesis๋ฅผ ๋’ท๋ฐ›์นจํ•ฉ๋‹ˆ๋‹ค. NF1์˜ ์ฒด์„ธํฌ ๋Œ์—ฐ๋ณ€์ด๋Š” ovarian serous carcinoma์˜ 30 % ์ด์ƒ,  melanoma์˜ 12-30 %, chronic myelomonocytic leukemia (CMML)์˜ 10-20 %, acute myeloid leukemia (AML)์˜ 7 %์—์„œ ํ™•์ธ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ Somatic NF1 mutation์€ ์—ฐ์กฐ์ง ์œก์ข…์—์„œ malignant peripheral nerve sheath tumor (MPNST)์— ๋Œ€ํ•œ ๋ณด์กฐ ์ง„๋‹จ ๊ธฐ์ค€์œผ๋กœ ํ™œ์šฉ๋˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํ˜„์žฌ NF1์˜ ๋ณ€์ด์™€ ๊ด€๋ จํ•˜์—ฌ ๊ณต์ธ๋œ ํ‘œ์ ์น˜๋ฃŒ์ œ๋Š” ์•Œ๋ ค์ ธ ์žˆ์ง€ ์•Š์œผ๋‚˜ ์ผ๋ถ€ ์•ฝ์ œ์— ๋Œ€ํ•˜์—ฌ ์ž„์ƒ์‹œํ—˜์ด ์ง„ํ–‰๋˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 

๊ฒ€์‚ฌ๊ฒฐ๊ณผ TP53 ์œ ์ „์ž์—์„œ p.(L45Afs*7) ๋ฐœ๊ฒฌ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. TP53 ์œ ์ „์ž๋Š” DNA์— ๊ฒฐํ•ฉํ•˜๊ณ  ๋‹ค์–‘ํ•œ ์„ธํฌ ์ŠคํŠธ๋ ˆ์Šค์— ๋ฐ˜์‘ํ•˜์—ฌ ์ „์‚ฌ๋ฅผ ํ™œ์„ฑํ™”ํ•˜์—ฌ ์„ธํฌ ์ฃผ๊ธฐ ์ •์ง€, ์„ธํฌ์ž๋ฉธ์‚ฌ ๋˜๋Š” DNA ๋ณต๊ตฌ๋ฅผ ์œ ๋„ํ•˜๋Š” p53 ์ข…์–‘ ์–ต์ œ ๋‹จ๋ฐฑ์งˆ์„ ์•”ํ˜ธํ™”ํ•ฉ๋‹ˆ๋‹ค. ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ๋ฐ›์ง€ ์•Š์€ ์„ธํฌ์—์„œ TP53์€ ubiquitin ์˜์กด์„ฑ ๋‹จ๋ฐฑ์งˆ ๋ถ„ํ•ด์— ๋Œ€ํ•œ ๊ธฐ์งˆ ์ธ์‹ ์ธ์ž์ธ MDM2๋ฅผ ํ†ตํ•œ ํ‘œ์  ๋ถ„ํ•ด์— ์˜ํ•ด ๋น„ํ™œ์„ฑ ์ƒํƒœ๋กœ ์œ ์ง€๋ฉ๋‹ˆ๋‹ค. TP53์˜ ๋ณ€ํ˜•์€ ๋‹จ๋ฐฑ์งˆ ๊ธฐ๋Šฅ์˜ ์†์‹ค๊ณผ ๋ณ€ํ˜• ๊ฐ€๋Šฅ์„ฑ์˜ ํš๋“์„ ์ดˆ๋ž˜ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ข…์–‘ ๋ฐœ์ƒ์— ๊ธฐ์—ฌํ•ฉ๋‹ˆ๋‹ค. TP53์˜ ์ƒ์‹์„  ๋Œ์—ฐ๋ณ€์ด๋Š” ์กฐ๊ธฐ ๋ฐœ๋ณ‘ ์•”๊ณผ ๊ด€๋ จ๋œ ๋ณตํ•ฉ ์œ ์ „์„ฑ ์•” ์†Œ์ธ ์žฅ์• ์ธ Li-Fraumeni ์ฆํ›„๊ตฐ์˜ ๊ทผ๋ณธ ์›์ธ์ž…๋‹ˆ๋‹ค. TP53์€ ์•” ๊ฒŒ๋†ˆ์—์„œ ๊ฐ€์žฅ ๋นˆ๋ฒˆํ•˜๊ฒŒ ๋Œ์—ฐ๋ณ€์ด๋˜๋Š” ์œ ์ „์ž๋กœ ๋ชจ๋“  ์•”์˜ ์•ฝ ์ ˆ๋ฐ˜์ด TP53 ๋Œ์—ฐ๋ณ€์ด๋ฅผ ๋‚˜ํƒ€๋ƒ…๋‹ˆ๋‹ค. ๋‚œ์†Œ์•”, ๋‘๊ฒฝ๋ถ€์•”, ์‹๋„์•”, ํ์˜ ํŽธํ‰์„ธํฌ์•”์€ ํŠนํžˆ TP53 ๋Œ์—ฐ๋ณ€์ด ๋น„์œจ์ด ๋†’์Šต๋‹ˆ๋‹ค(60-90%). TP53 ๋Œ์—ฐ๋ณ€์ด์˜ ์•ฝ 2/3๋Š” missense mutation ์ด๋ฉฐ ์ฝ”๋ˆ R158, R175, Y220, R248, R273 ๋ฐ R282์—์„œ์˜ ์น˜ํ™˜์„ ํฌํ•จํ•˜์—ฌ ๋ช‡ ๊ฐ€์ง€ ์žฌ๋ฐœ์„ฑ missense mutation์ด ์ผ๋ฐ˜์ ์ž…๋‹ˆ๋‹ค. TP53์˜ ๋ฐ˜๋ณต์ ์ธ missense mutation์€ TP53์ด DNA์— ๊ฒฐํ•ฉํ•˜๋Š” ๋Šฅ๋ ฅ์„ ๋น„ํ™œ์„ฑํ™”ํ•˜๊ณ  ๊ฒฐ๊ณผ์ ์œผ๋กœ ํ‘œ์  ์œ ์ „์ž์˜ ์ „์‚ฌ๋ฅผ ํ™œ์„ฑํ™”ํ•ฉ๋‹ˆ๋‹ค. p53 ์žฌํ™œ์„ฑํ™”์ œ์ธ PC14586์€ TP53 Y220C ๋Œ์—ฐ๋ณ€์ด๊ฐ€ ์žˆ๋Š” ์ง„ํ–‰์„ฑ ์ข…์–‘์— ๋Œ€ํ•ด FDA๋กœ๋ถ€ํ„ฐ ํŒจ์ŠคํŠธํŠธ๋ž™์œผ๋กœ  ์ง€์ •(2020)์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. FDA๋Š” TP53 ๋Œ์—ฐ๋ณ€์ด๋ฅผ ๋ณด์œ ํ•œ ๊ณจ์ˆ˜์ดํ˜•์„ฑ์ฆํ›„๊ตฐ(MDS) ๋ฐ ๊ธ‰์„ฑ ๊ณจ์ˆ˜์„ฑ ๋ฐฑํ˜ˆ๋ณ‘ ํ™˜์ž(AML)์— ๋Œ€ํ•ด p53 ์žฌํ™œ์„ฑํ™”์ œ์ธ APR-246 ๋‹จ๋… ๋ฐ azacitidine๊ณผ์˜ ๋ณ‘์šฉ์š”๋ฒ•(2020)์— ๋Œ€ํ•˜์—ฌ ์‹ ์† ์ง€์ •(2019)์„ ๋ถ€์—ฌํ•œ ๋ฐ” ์žˆ์Šต๋‹ˆ๋‹ค. ๋ณ€์ดํ˜•์˜ TP53์ด  wild type์œผ๋กœ ํ™œ์„ฑ์ด ํšŒ๋ณต๋˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ์—ฐ๊ตฌ ์š”๋ฒ• ์™ธ์—๋„ ํ•ฉ์„ฑ ์น˜์‚ฌ์œจ(synthetic lethality)์„ ์œ ๋„ํ•˜๋Š” ํ™”ํ•ฉ๋ฌผ๋„ ์ž„์ƒ ํ‰๊ฐ€ ์ค‘์ž…๋‹ˆ๋‹ค. TP53 ๋Œ์—ฐ๋ณ€์ด๋Š” AML, MDS, ๊ณจ์ˆ˜์ฆ์‹์„ฑ ์‹ ์ƒ๋ฌผ(MPN), ๋งŒ์„ฑ ๋ฆผํ”„๊ตฌ์„ฑ ๋ฐฑํ˜ˆ๋ณ‘(CLL)์„ ํฌํ•จํ•œ ๋‹ค์–‘ํ•œ ํ˜ˆ์•ก์•”์—์„œ ๋‚˜์œ ์˜ˆํ›„์™€ ๊ด€๋ จ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. ์™ธํˆฌ ์„ธํฌ ๋ฆผํ”„์ข… (mantle cell lymphoma)์—์„œ TP53 ๋Œ์—ฐ๋ณ€์ด๋Š” ์กฐํ˜ˆ ์„ธํฌ ์ด์‹์„ ํฌํ•จํ•œ ๊ธฐ์กด ์š”๋ฒ•์œผ๋กœ ์น˜๋ฃŒํ•  ๋•Œ ๋ถˆ๋Ÿ‰ํ•œ ์˜ˆํ›„์™€ ๊ด€๋ จ์ด ์žˆ์Šต๋‹ˆ๋‹ค.

๊ฒ€์‚ฌ๊ฒฐ๊ณผ CCND1 ์œ ์ „์ž์—์„œ amplification ๋ฐœ๊ฒฌ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. CCND1 ์œ ์ „์ž๋Š” CCND2 ๋ฐ CCND3๋„ ํฌํ•จํ•˜๋Š” ๊ณ ๋„๋กœ ๋ณด์กด๋œ D-์‚ฌ์ดํด๋ฆฐ ๊ณ„์—ด์˜ ๊ตฌ์„ฑ์›์ธ ์‚ฌ์ดํด๋ฆฐ D1 ๋‹จ๋ฐฑ์งˆ์„ ์ธ์ฝ”๋”ฉํ•ฉ๋‹ˆ๋‹ค. Dํ˜• ์‚ฌ์ดํด๋ฆฐ์€ ์‚ฌ์ดํด๋ฆฐ ์˜์กด์„ฑ ํ‚ค๋‚˜์ œ(CDK), ํŠนํžˆ CDK4 ๋ฐ CDK6์— ๊ฒฐํ•ฉํ•˜๊ณ  ํ™œ์„ฑํ™”ํ•จ์œผ๋กœ์จ ์„ธํฌ ์ฃผ๊ธฐ ์ง„ํ–‰์„ ์กฐ์ ˆํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ์œผ๋ฉฐ, ์ด๋Š” ๋ง๋ง‰๋ชจ์„ธํฌ์ข…(RB1) ๋‹จ๋ฐฑ์งˆ์˜ ์ธ์‚ฐํ™” ๋ฐ ๋ถˆํ™œ์„ฑํ™”๋ฅผ ์œ ๋„ํ•ฉ๋‹ˆ๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ, RB1 ๋น„ํ™œ์„ฑํ™”๋Š” E2F ์ „์‚ฌ ์ธ์ž ํ™œ์„ฑํ™” ๋ฐ ์„ธํฌ G1/S ์ƒ์ „์ด๋ฅผ ์ดˆ๋ž˜ํ•˜์—ฌ ์ข…์–‘ ํ˜•์„ฑ์—์„œ ๊ด€์ฐฐ๋˜๋Š” ์ผ๋ฐ˜์ ์ธ ์ด๋ฒคํŠธ์ธ ์„ธํฌ ์ฃผ๊ธฐ ์ง„ํ–‰์„ ์ดˆ๋ž˜ํ•ฉ๋‹ˆ๋‹ค. Dํ˜• ์‚ฌ์ดํด๋ฆฐ์˜ ์ด์ƒ์ด ์ข…์–‘ ์ง„ํ–‰์„ ์ด‰์ง„ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๊ด€์ฐฐ๋˜์–ด CCND1์— ๋Œ€ํ•œ ๋ฐœ์•”์„ฑ ์—ญํ• ์„ ์‹œ์‚ฌํ•ฉ๋‹ˆ๋‹ค. ๋Œ์—ฐ๋ณ€์ด, ์ฆํญ ๋ฐ ์—ผ์ƒ‰์ฒด ์ „์œ„๋ฅผ ํฌํ•จํ•˜์—ฌ CCND1์— ๋Œ€ํ•œ ๋ฐ˜๋ณต์ ์ธ ์ฒด์„ธํฌ ๋ณ€ํ˜•์ด ๋งŽ์€ ์•” ์œ ํ˜•์—์„œ ๊ด€์ฐฐ๋ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€๊ฒฝ์˜ ์ผ๋ฐ˜์ ์ธ ๋ฉ”์ปค๋‹ˆ์ฆ˜์€ cyclin D1 ๋‹จ๋ฐฑ์งˆ์˜ ๋ฐœํ˜„๊ณผ ํ•ต ๊ตญ์†Œํ™”๋ฅผ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์žฌ๋ฐœ์„ฑ ์ฒด์„ธํฌ ๋Œ์—ฐ๋ณ€์ด์—๋Š” ์ฝ”๋ˆ T286 ๋ฐ P287์—์„œ์˜ ๋ฏธ์Šค์„ผ์Šค ๋Œ์—ฐ๋ณ€์ด ๋ฐ ์ž๊ถ์•”์˜ ์•ฝ 33%๊ฐ€ ํ’๋ถ€ํ•œ c-๋ง๋‹จ ์ ˆ๋‹จ ๋Œ์—ฐ๋ณ€์ด, ๋ฐ ๋งจํ‹€ ์„ธํฌ ๋ฆผํ”„์ข…(MCL)์˜ ์•ฝ 50%๊ฐ€ ํ’๋ถ€ํ•œ Y44์—์„œ์˜ ๋ฏธ์Šค์„ผ์Šค ๋Œ์—ฐ๋ณ€์ด๊ฐ€ ํฌํ•จ๋ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋Œ์—ฐ๋ณ€์ด๋Š” ์ธ์‚ฐํ™” ์˜์กด์„ฑ ํ•ต ์ˆ˜์ถœ ๋ฐ ๋‹จ๋ฐฑ์งˆ ๋ถ„ํ•ด๋ฅผ ์ฐจ๋‹จํ•ฉ๋‹ˆ๋‹ค. CCND1์€ ์‹๋„์•”์˜ ์ตœ๋Œ€ 35%, ๋‘๊ฒฝ๋ถ€์•”์˜ 20-30%, ์œ ๋ฐฉ์•”, ํŽธํ‰ ํ์•” ๋ฐ ๋ฐฉ๊ด‘์•”์˜ 10-20%๋ฅผ ํฌํ•จํ•˜์—ฌ ๋งŽ์€ ์•” ์œ ํ˜•์—์„œ ๋ฐ˜๋ณต์ ์œผ๋กœ ์ฆํญ๋ฉ๋‹ˆ๋‹ค. MCL์€ CCND1์„ ๋ฉด์—ญ๊ธ€๋กœ๋ถˆ๋ฆฐ ์ค‘์‡„(IgH) ์‚ฌ์Šฌ ์œ ์ „์ž์— ๋ณ‘์น˜์‹œํ‚ค๋Š” ์žฌ๋ฐฐ์—ด์ธ t(11;14)(q13;q13) ์ „์œ„๋ฅผ ์œ ์ „์ ์œผ๋กœ ํŠน์ง•์œผ๋กœ ํ•ฉ๋‹ˆ๋‹ค. ์ด ์žฌ๋ฐฐ์—ด์€ cyclin D1์˜ ๊ตฌ์„ฑ์  ๋ฐœํ˜„์„ ์œ ๋„ํ•˜๊ณ  MCL ๋ฐœ๋ณ‘์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค. ํ˜„์žฌ CCND1 ์ด์ƒ์— ๋Œ€ํ•ด ์Šน์ธ๋œ ์น˜๋ฃŒ๋ฒ•์€ ์—†์Šต๋‹ˆ๋‹ค.

๊ฒ€์‚ฌ๊ฒฐ๊ณผ FGF19 ์œ ์ „์ž์—์„œ amplification ๋ฐœ๊ฒฌ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. FGF19 ์œ ์ „์ž๋Š” 22 ๊ฐœ์˜ ๊ตฌ์„ฑ์›์œผ๋กœ ๊ตฌ์„ฑ๋œ FGF protein family ์ค‘ ํ•˜๋‚˜์ธ fibroblast growth factor 19 ๋‹จ๋ฐฑ์งˆ์„ ์•”ํ˜ธํ™”ํ•ฉ๋‹ˆ๋‹ค. FGF ๋‹จ๋ฐฑ์งˆ์€  tyrosine kinases์ธ fibroblast growth factor receptor (FGFR) ๊ณ„์—ด์˜ ligand๋กœ FRFG์˜ ํ™œ์„ฑํ™”๋ฅผ ์ผ์œผ์ผœ RAS/RAF/MEK/ERK, PI3K/AKT/MTOR, PLC/PKC, and JAK/STAT pathway๋ฅผ ํ†ตํ•˜์—ฌ ์„ธํฌ์˜ ์„ฑ์žฅ, ์ด๋™, ์ƒ์กด์— ์˜ํ–ฅ์„ ๋ฏธ์นฉ๋‹ˆ๋‹ค. FGF19 amplification์€ ์‹๋„์•”์˜ ์•ฝ 35 %, ๋‘๊ฒฝ๋ถ€์•”์˜ 23 %, ์นจ์œค์„ฑ ์œ ๋ฐฉ์•”, ๋‹ด๊ด€์•”, ํŽธํ‰ ํ ๋ฐ ๋ฐฉ๊ด‘์•”์˜ 10-15 %, ํ‘์ƒ‰์ข…, ๊ฐ„์•”,๋‚œ์†Œ์•”, ์œ„์•”์˜ 5-7 %์—์„œ ๊ด€์ฐฐ๋œ๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ์Šต๋‹ˆ๋‹ค. FGF19 ๊ณผ๋ฐœํ˜„์€ ๊ฐ„์„ธํฌ ์•”์ข…์˜ ๋ฐœ๋‹ฌ ๋ฐ ์ข…์–‘ ์ง„ํ–‰๊ณผ ๊ด€๋ จ์ด ์žˆ์Šต๋‹ˆ๋‹ค. ํ˜„์žฌ, FGF19 ์ด์ƒ์— ๋Œ€ํ•ด ์Šน์ธ ๋œ ์น˜๋ฃŒ๋ฒ•์€ ์—†์Šต๋‹ˆ๋‹ค. fisogatinib (BLU-554)์„ ํฌํ•จํ•œ ์„ ํƒ์  ๋น„๊ฐ€์—ญ์  FGFR4 ์–ต์ œ์ œ๋Š” ํ˜„์žฌ ์ž„์ƒ์‹œํ—˜ ํ‰๊ฐ€ ์ค‘์ž…๋‹ˆ๋‹ค. ์ง„ํ–‰์„ฑ ๊ฐ„์„ธํฌ ์•”์ข… ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ fisogatinib์˜ 1 ์ƒ ์ž„์ƒ ์—ฐ๊ตฌ์—์„œ ๋“ฑ๋ก ๋œ 115 ๋ช…์˜ ํ™˜์ž ์ค‘ 63 %๊ฐ€ ๋ฉด์—ญ์กฐ์งํ™”ํ•™์—ผ์ƒ‰์—์„œ FGF19 ์–‘์„ฑ์ด์—ˆ์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ, ํ‰๊ฐ€๊ฐ€๋Šฅํ•œ ์กฐ์ง์ด ์žˆ๋Š” 53 ๋ช…์˜ ํ™˜์ž์—์„œ 96%๊ฐ€ FGFR4 ๋ฐ KLB์˜ mRNA ๋ฐœํ˜„์„ ๋‚˜ํƒ€๋ƒˆ์Šต๋‹ˆ๋‹ค. ๋ฐ˜์‘์— ๋Œ€ํ•ด ํ‰๊ฐ€ํ•  ์ˆ˜์žˆ๋Š” FGF19 ์–‘์„ฑ ํ™˜์ž์—์„œ fisogatinib์— ๋Œ€ํ•ด ๊ด€์ฐฐ ๋œ ์ด ์ „์ฒด ๋ฐ˜์‘๋ฅ ์€ 17 %์˜€์Šต๋‹ˆ๋‹ค.

๊ฒ€์‚ฌ๊ฒฐ๊ณผ FGF3 ์œ ์ „์ž์—์„œ amplification ๋ฐœ๊ฒฌ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. FGF3 ์œ ์ „์ž๋Š” 22 ๊ฐœ์˜ ๊ตฌ์„ฑ์›์œผ๋กœ ๊ตฌ์„ฑ๋œ FGF protein family ์ค‘ ํ•˜๋‚˜์ธ fibroblast growth factor 3 ๋‹จ๋ฐฑ์งˆ์„ ์•”ํ˜ธํ™”ํ•ฉ๋‹ˆ๋‹ค. FGF ๋‹จ๋ฐฑ์งˆ์€  tyrosine kinases์ธ fibroblast growth factor receptor (FGFR) ๊ณ„์—ด์˜ ligand๋กœ FRFG์˜ ํ™œ์„ฑํ™”๋ฅผ ์ผ์œผ์ผœ RAS/RAF/MEK/ERK, PI3K/AKT/MTOR, PLC/PKC, and JAK/STAT pathway๋ฅผ ํ†ตํ•˜์—ฌ ์„ธํฌ์˜ ์„ฑ์žฅ, ์ด๋™, ์ƒ์กด์— ์˜ํ–ฅ์„ ๋ฏธ์นฉ๋‹ˆ๋‹ค. ํŠนํžˆ, FGF3์€ FGFR1 ๋ฐ FGFR2 ๋‘˜ ๋‹ค์— ๊ฒฐํ•ฉํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์Šต๋‹ˆ๋‹ค. FGF3 ์ฆํญ์€ ์‹๋„์•”์˜ ์•ฝ 35 %, ๋‘๊ฒฝ๋ถ€์•”์˜ 24 %, ์นจ์œค์„ฑ ์œ ๋ฐฉ์•”์ข…, ํ์˜ ํŽธํ‰์ƒํ”ผ์•”์ข… ๋ฐ ๋ฐฉ๊ด‘์•”์˜ 10-15 %, ๋‹ด๊ด€์•”, ํ‘์ƒ‰์ข…, ๊ฐ„, ๋‚œ์†Œ, ์œ„์˜ ์•”์ข…์˜ 5-10 %์—์„œ ๊ด€์ฐฐ๋ฉ๋‹ˆ๋‹ค. FGF3 ๊ณผ๋ฐœํ˜„์€ ๋น„์†Œ ์„ธํฌ ํ์•” (NSCLC) ๋ฐœ์ƒ๊ณผ ๊ด€๋ จ์ด ์žˆ์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ ๊ฐ„์„ธํฌ ์•”์ข…์—์„œ ์ข…์–‘ ์ „์ด ๋ฐ ์žฌ๋ฐœ๊ณผ๋„ ๊ด€๋ จ์ด ์žˆ์Šต๋‹ˆ๋‹ค. ํ˜„์žฌ๊นŒ์ง€, FGF3์˜ ๋ณ€์ด์™€ ์—ฐ๊ด€๋˜์–ด ์Šน์ธ๋œ ์น˜๋ฃŒ๋ฒ•์€ ์—†์Šต๋‹ˆ๋‹ค. 
๊ทธ ์™ธ ํ•„์ˆ˜ ๋ฐ ์„ ํƒ์œ ์ „์ž์—์„œ Tier 1-3์— ํ•ด๋‹นํ•˜๋Š” ๋ณ€์ด๊ฐ€ ๋ฐœ๊ฒฌ๋˜์ง€ ์•Š์•˜์Šต๋‹ˆ๋‹ค. Tier 4 (benign or likely benign)์— ํ•ด๋‹นํ•˜๋Š” ๋ณ€์ด๋Š” ๋ณด๊ณ ๋˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. 

COMMENT
1. ๋ณธ ๊ฒฐ๊ณผ๋Š” ์ฐธ๊ณ  ์œ ์ „์ž ์ •๋ณด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ข…์–‘ ์กฐ์ง๋งŒ์œผ๋กœ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ์ž…๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ฒด์„ธํฌ ๋Œ์—ฐ๋ณ€์ด์™€ ์ƒ์‹์„ธํฌ ๋Œ์—ฐ๋ณ€์ด์˜ ํ™•์‹คํ•œ ๊ตฌ๋ถ„์ด ์–ด๋ ค์šธ ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ƒ์‹์„ธํฌ ๋Œ์—ฐ๋ณ€์ด์˜ ๊ฒฝ์šฐ ์ข…์–‘๊ณผ์˜ ๊ด€๋ จ์„ฑ์ด ์žˆ๋”๋ผ๋„ ๋ณธ ๊ฒฐ๊ณผ์—์„œ ๋ณด๊ณ ๋˜์ง€ ์•Š์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

2. ์ „์ฒด ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ ๋ฐ ๋ณ€์ด์— ๋Œ€ํ•œ ์ •๋ณด๋Š” Supplementary report (CIS server)๋ฅผ ์ฐธ์กฐํ•˜์‹œ๊ธฐ ๋ฐ”๋ž๋‹ˆ๋‹ค. 

TESTED GENE LIST

ํ•„์ˆ˜ ์œ ์ „์ž :
ALK, BRCA1, BRCA2, BRAF, EGFR, HER2, IDH1, IDH2, KIT, KRAS, MYC(C-myc), N-myc(MYCN), NRAS, PDGFRA, BRAF, MYD88, TP53 

์„ ํƒ ์œ ์ „์ž : 
ABL1, A1CF, ABCB1, ABL1, ABL2, ABRAXAS1, ACSM2B, ACVR1, ACVR1B, ACVR2A, ADAM18, ADAMTS12, ADAMTS2, AKT1, AKT2, AKT3, AMER1, ANO4, APC, AR, ARAF, ARHGAP35, ARID1A, ARID1B, ARID2, ARID5B, ARMC4, ASXL1, ASXL2, ATM, ATP1A1, ATR, ATRX, AURKA, AURKC, AXIN1, AXIN2, AXL, B2M, BAP1, BARD1, BCL2, BCL2L12, BCL6, BCOR, BCR, BLM, BMP5, BMPR2, BRINP3, BRIP1, BTK, C6, C8A, C8B, CACNA1D, CALR, CANX, CARD11, CASP8, CASR, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD163, CD274, CD276, CD79B, CDC73, CDH1, CDH10, CDK12, CDK4, CDK6, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CHD4, CHEK1, CHEK2, CIC, CIITA, CNTN6, CNTNAP4, CNTNAP5, COL11A1, CREBBP, CSF1R, CSMD3, CTCF, CTLA4, CTNNB1, CTNND2, CUL1, CUL3, CUL4A, CUL4B, CYLD, CYP2C9, CYP2D6, CYSLTR2, DAXX, DCAF4L2, DCDC1, DDR1, DDR2, DDX3X, DGCR8, DICER1, DNMT3A, DOCK3, DPYD, DROSHA, DSC1, DSC3, E2F1, EIF1AX, ELF3, EMSY, ENO1, EP300, EPAS1, EPCAM, EPHA2, ERAP1, ERAP2, ERBB3, ERBB4, ERCC2, ERCC4, ERCC5, ERRFI1, ESR1, ETV6, EZH2, FAM135B, FANCA, FANCC, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCL, FANCM, FAS, FAT1, FBXW7, FGF19, FGF23, FGF3, FGF4, FGF7, FGF9, FGFR1, FGFR2, FGFR3, FGFR4, FLT3, FLT4, FOXA1, FOXL2, FOXO1, FUBP1, FYN, GALNT17, GATA2, GATA3, GLI1, GLI3, GNA11, GNA13, GNAQ, GNAS, GPR158, GPS2, GRID2, H3F3A, H3F3B, HCN1, HDAC2, HDAC9, HIF1A, HIST1H2BD, HIST1H3B, HLA-A, HLA-B, HLA-C, HNF1A, HRAS, ID3, IGF1R, IKBKB, IL6ST, IL7R, INPP4B, IRF4, IRS4, JAK1, JAK2, JAK3, KCND2, KCNH7, KDM5C, KDM6A, KDR, KEAP1, KEL, KIR3DL1, KLF4, KLF5, KLHL13, KMT2A, KMT2B, KMT2C, KMT2D, KNSTRN, KRTAP2-1, KRTAP6-2, LARP4B, LATS1, LATS2, LRRC7, MAGOH, MAP2K1, MAP2K2, MAP2K4, MAP2K7, MAP3K1, MAP3K4, MAPK1, MAPK8, MARCO, MAX, MCL1, MDM2, MDM4, MECOM, MED12, MEF2B, MEN1, MET, MGA, MITF, MLH1, MLH3, MPL, MRE11, MSH2, MSH3, MSH6, MTAP, MTOR, MTUS2, MUTYH, MYCL, MYOD1, NBN, NCOR1, NF1, NF2, NFE2L2, NLRC5, NOL4, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NRXN1, NSD2, NT5C2, NTRK1, NTRK2, NTRK3, NUP93, NYAP2, OR10G8, OR2G6, OR2L13, OR2L2, OR2L8, OR2M3, OR2T3, OR2T33, OR2T4, OR2W3, OR4A15, OR4C15, OR4C6, OR4M1, OR4M2, OR5D18, OR5F1, OR5L1, OR5L2, OR6F1, OR8H2, OR8I2, OR8U1, ORC4, PAK5, PALB2, PARP1, PARP2, PARP3, PARP4, PAX5, PBRM1, PCBP1, PCDH17, PDCD1, PDCD1LG2, PDE1A, PDE1C, PDGFRB, PDIA3, PGD, PHF6 ,PIK3C2B, PIK3CA, PIK3CB, PIK3CD, PIK3CG, PIK3R1, PIK3R2, PIM1, PLCG1, PLXDC2, PMS1, PMS2, POLD1, POLE, POM121L12, POT1, PPFIA2, PPM1D, PPP2R1A, PPP2R2A, PPP6C, PRDM1, PRDM9, PRKACA, PRKAR1A, PSMB10, PSMB8, PSMB9, PTCH1, PTEN, PTPN11, PTPRD, PTPRT, PXDNL, RAC1, RAD50, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAF1, RARA, RASA1, RASA2, RB1, RBM10, RBP3, RECQL4, REG1A, REG1B, REG3A, REG3G, RET, RGS7, RHEB, RHOA, RICTOR, RIT1, RNASEH2A, RNASEH2B, RNASEH2C, RNF43, ROS1, RPA1, RPL10, RPL22, RPL5, RPS6KB1, RPTN, RPTOR, RUNDC3B, RUNX1, RUNX1T1, SDHA, SDHB, SDHC, SDHD, SETBP1, SETD2, SF3B1, SH3RF2, SIX1, SIX2, SLC15A2, SLC8A1, SLCO1B3, SLX4, SMAD2, SMAD4, SMARCA4, SMARCB1, SMC1A, SMO, SNCAIP, SOCS1, SOS1, SOX2, SOX9, SPEN, SPOP, SRC, SRSF2, STAG2, STAT1, STAT3, STAT5B, STAT6, STK11, SUFU, SYT10, SYT16, TAF1, TAP1, TAP2, TAPBP, TBX3, TCF7L2, TERT, TET2, TGFBR1, TGFBR2, TMEM132D, TNFAIP3, TNFRSF14, TOP1, TP63, TPMT, TPP2, TPTE, TRHDE, TRIM48, TRIM51, TRRAP, TSC1, TSC2, TSHR, U2AF1, UGT1A1, USP8, USP9X, VHL, WAS, WT1, XPO1, XRCC2, XRCC3, YAP1, YES1, ZBTB20, ZFHX3, ZIM3, ZMYM3, ZNF217, ZNF429, ZNF479, ZNF536, ZRSR2

TEST INFORMATION  
NGS (Next Generation Sequencing) - Ion S5XL Sequencer
Oncomineยฎ Comprehensive Plus Panel
(DNA panel:  165 Hotspot genes (hotspot coverage), 227 Full-length genes, 333 Copy number genes, 51 Gene fusions,  Oncomine Reporter Software Version:  v.5.7 with Reference genome Hg19)

SEQUENCING QC MATRIX
Genomic DNA isolated form FFPE tissue
Tumor purity (cellularity):  70 %
Sample DNA QC:  Pass
Sample RNA QC:  Pass
Library QC:  Pass
Sequencing QC:  Pass
Mean target depth:  3316 x 
On target rate:  95.13 %
Uniformity:  96.52 %       

๊ฒ€์‚ฌ์˜ ํ•œ๊ณ„
๋ณธ ๊ฒ€์‚ฌ๋Š” DNA ์—ผ๊ธฐ์„œ์—ด๋ถ„์„๋ฒ•์œผ๋กœ ์‹œํ–‰๋˜์—ˆ์œผ๋ฉฐ, ๊ฒ€์‚ฌ์— ํฌํ•จ๋˜์ง€ ์•Š์€ exon ์˜์—ญ์— ์กด์žฌํ•˜๋Š” ๋ณ€์ด๋Š” ๊ฒ€์ถœํ•  ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค. SNV ๋ฐ small indel์˜ ๊ฒ€์ถœํ•œ๊ณ„๋Š” ์•ฝ 5 % ์ž…๋‹ˆ๋‹ค. Copy number aberration์€ ๋ถ€์ˆ˜์ ์œผ๋กœ ์ œ๊ณตํ•˜๋Š” ๊ฒฐ๊ณผ์ž…๋‹ˆ๋‹ค. MYC/NMYC, MET amplification์€ 4 copy gain๋ถ€ํ„ฐ ๊ฒ€์ถœ ๊ฐ€๋Šฅํ•˜๋ฉฐ, tumor purity์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ผ๋ถ€ target region๋“ค์€ coverage๊ฐ€ ๋–จ์–ด์งˆ ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ์Šต๋‹ˆ๋‹ค. ํ•ด๋‹น ๊ฒ€์‚ฌ์—์„œ ๋ฐœ๊ฒฌ๋œ ๋ณ€์ด๋Š” sanger sequencing๋“ฑ ๋‹ค๋ฅธ ๊ฒ€์‚ฌ๋ฒ•์„ ์ด์šฉํ•œ ์žฌํ™•์ธ์„ ์‹œํ–‰ํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. ๋ณธ ๊ฒ€์‚ฌ๋กœ๋Š” germ-line๊ณผ somatic ๋ณ€์ด๋ฅผ ๊ฐ๋ณ„ํ•  ์ˆ˜ ์—†์œผ๋ฉฐ, hereditary cancer syndrome๊ณผ ๊ด€๋ จ๋˜์–ด ์žˆ๋Š” ์œ ์ „์ž์—์„œ mutation์˜ variant allele frequency๊ฐ€ 50 % ํ˜น์€ 100 %์— ๊ฐ€๊นŒ์šด ๊ฒฝ์šฐ germline mutation์˜ ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ์Šต๋‹ˆ๋‹ค.
  • Output
{
  "reported_result": [
    {
      "path_no": "S 230017454",
      "exam_name": "NGS๊ธฐ๋ฐ˜ ์œ ์ „์žํŒจ๋„๊ฒ€์‚ฌ(๊ณ ํ˜•์•”, Level 2 + RNA Fusion)",
      "sequencer": "Ion S5XL",
      "spec_site": "liver",
      "panel_name": "Oncomineยฎ Comprehensive Plus Panel",
      "spec_method": "USG liver bx",
      "tumor_purity": "70 %",
      "clinical_finding": "breast cancer r/o liver meta",
      "specimen_adequacy": "Adequate",
      "reference_genome_ver": "Hg19",
      "spec_fixation_method": "FFPE"
    },
    {
      "gene": "ATM",
      "gene_finding_result": "๋ฏธ๊ฒ€์ถœ",
      "cancer_mutations_found": "์œ ๋ฐฉ์•” ๊ด€๋ จ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ"
    },
    {
      "gene": "BRCA1",
      "gene_finding_result": "๋ฏธ๊ฒ€์ถœ",
      "cancer_mutations_found": "์œ ๋ฐฉ์•” ๊ด€๋ จ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ"
    },
    {
      "gene": "PALB2",
      "gene_finding_result": "๋ฏธ๊ฒ€์ถœ",
      "cancer_mutations_found": "์œ ๋ฐฉ์•” ๊ด€๋ จ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ"
    },
    {
      "gene": "BRCA2",
      "gene_finding_result": "๋ฏธ๊ฒ€์ถœ",
      "cancer_mutations_found": "์œ ๋ฐฉ์•” ๊ด€๋ จ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ"
    },
    {
      "gene": "CDH1",
      "gene_finding_result": "๋ฏธ๊ฒ€์ถœ",
      "cancer_mutations_found": "์œ ๋ฐฉ์•” ๊ด€๋ จ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ"
    },
    {
      "gene": "PTEN",
      "gene_finding_result": "๋ฏธ๊ฒ€์ถœ",
      "cancer_mutations_found": "์œ ๋ฐฉ์•” ๊ด€๋ จ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ"
    },
    {
      "gene": "CHEK2",
      "gene_finding_result": "๋ฏธ๊ฒ€์ถœ",
      "cancer_mutations_found": "์œ ๋ฐฉ์•” ๊ด€๋ จ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ"
    },
    {
      "gene": "STK11",
      "gene_finding_result": "๋ฏธ๊ฒ€์ถœ",
      "cancer_mutations_found": "์œ ๋ฐฉ์•” ๊ด€๋ จ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ"
    },
    {
      "gene": "ERBB2",
      "gene_finding_result": "๋ฏธ๊ฒ€์ถœ",
      "cancer_mutations_found": "์œ ๋ฐฉ์•” ๊ด€๋ จ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ"
    },
    {
      "gene": "NBN",
      "gene_finding_result": "๋ฏธ๊ฒ€์ถœ",
      "cancer_mutations_found": "์œ ๋ฐฉ์•” ๊ด€๋ จ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ"
    },
    {
      "gene": "PIK3CA",
      "tier": "Tier 1",
      "aa_change": "p.(H1047R)",
      "dna_change": "c.3140A>G",
      "variant_af": "60.10 %",
      "total_depth": "2000",
      "exonic_function": "missense",
      "cancer_mutations_found": "VARIANTS FOUND"
    },
    {
      "gene": "NF1",
      "tier": "Tier 2",
      "aa_change": "p.(Y2285Tfs*5)",
      "dna_change": "c.6852_6855delTTAC",
      "variant_af": "71.28 %",
      "total_depth": "1978",
      "exonic_function": "Frameshiftdeletion",
      "cancer_mutations_found": "VARIANTS FOUND"
    },
    {
      "gene": "TP53",
      "tier": "Tier 2",
      "aa_change": "p.(L45Afs*7)",
      "dna_change": "c.132_133insG",
      "variant_af": "67.79 %",
      "total_depth": "1984",
      "exonic_function": "Frameshiftinsertion",
      "cancer_mutations_found": "VARIANTS FOUND"
    },
    {
      "gene": "KEAP1",
      "tier": "Tier 2",
      "aa_change": "p.(A270Cfs*6)",
      "dna_change": "c.807_810delGGCC",
      "variant_af": "81.46 %",
      "total_depth": "1985",
      "exonic_function": "Frameshiftdeletion",
      "cancer_mutations_found": "VARIANTS FOUND"
    },
    {
      "gene": "CCND1",
      "tier": "Tier 2",
      "cnv_status": "amplification",
      "estimated_copy_number": "8.08",
      "cancer_mutations_found": "VARIANTS FOUND"
    },
    {
      "gene": "FGF19",
      "tier": "Tier 2",
      "cnv_status": "amplification",
      "estimated_copy_number": "8.02",
      "cancer_mutations_found": "VARIANTS FOUND"
    },
    {
      "gene": "FGF3",
      "tier": "Tier 2",
      "cnv_status": "amplification",
      "estimated_copy_number": "6.69",
      "cancer_mutations_found": "VARIANTS FOUND"
    },
    {
      "gene": "FGF4",
      "tier": "Tier 2",
      "cnv_status": "amplification",
      "estimated_copy_number": "6.61",
      "cancer_mutations_found": "VARIANTS FOUND"
    },
    {
      "gene": "EMSY",
      "tier": "Tier 2",
      "cnv_status": "amplification",
      "estimated_copy_number": "5.4",
      "cancer_mutations_found": "VARIANTS FOUND"
    },
    {
      "gene": "MAP3K1",
      "tier": "Tier 3",
      "aa_change": "p.(H1004Lfs*78)",
      "dna_change": "c.3011delA",
      "variant_af": "62.71 %",
      "total_depth": "1137",
      "exonic_function": "Frameshiftdeleetion",
      "cancer_mutations_found": "VARIANTS FOUND"
    },
    {
      "gene": "NCOR1",
      "tier": "Tier 3",
      "aa_change": "p.(G1669Efs*93)",
      "dna_change": "c.5006delG",
      "variant_af": "72.87 %",
      "total_depth": "1176",
      "exonic_function": "Frameshiftdeletion",
      "cancer_mutations_found": "VARIANTS FOUND"
    }
  ]
}

How to use

Environment

python = "^3.10"
peft = "^0.13.2"
trl = "^0.12.1"
vllm = "^0.8.3"
gunicorn = "^21.2.0"
packaging = "^24.0"
ninja = "^1.11.1.1"
fastapi = "^0.115.12"
uvicorn = {extras = ["standard"], version = "^0.34.0"}

Inference with vLLM

  • Serving
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Meta-Llama-3-8B \
  --adapter-path techlab-khc/Llama-pathology-ngs-level-2 \
  --gpu-memory-utilization 0.9
  • Request
import requests

prompt = '''
{report text}
'''

response = requests.post(
    "http://localhost:8000/v1/completions",
    headers={"Content-Type": "application/json"},
    json={
        "model": "meta-llama/Meta-Llama-3-8B",
        "prompt": prompt,
        "max_tokens": 1000,
        "temperature": 0.0
    }
)

print(response.json()["choices"][0]["text"])

License

Disclaimer

  • This adapter is intended for research purposes only.
  • Any clinical or diagnostic decision should be made by qualified professionals. Outputs are for reference, not for direct medical or clinical application.

Contact

For questions, bug reports, or collaboration, please open an issue in this repository.

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