Unmet Needs

최근 screening된 논문 80편이 시사하는 unmet needs를 위키 내부 근거로 정리했습니다.

개요

본 분석은 2026년 9월부터 11월 사이에 발표된 80편의 최신 문헌을 근거로 합니다. 이 자료들은 주로 NSCLC, SCLC, 고형암 및 혈액암 분야에서 표적 치료제(ADC, TKI), 면역항암제(Ici)의 새로운 조합 요법, 그리고 AI 기반 영상 분석(Radiomics, Deep Learning)과 정밀 진단 기술의 발전을 다루고 있습니다. 전반적으로, 기존 표준 치료에 대한 내성 극복, 난치성 하위 그룹(예: EGFR Exon 20 Insertion, RAS-mutant)에 대한 표적 치료 확립, 그리고 영상 및 유전체 데이터를 활용한 개인화된 예후 예측과 치료 반응 모니터링이 주요 흐름입니다.

주요 unmet needs

1. 면역항암제(Ici) 내성 기전의 규명 및 극복 전략의 부재 기존 Checkpoint Inhibitor(CPI) 단일 요법이나 표준 병용 요법에 대한 내성은 여전히 해결되지 않은 핵심 문제입니다. 여러 연구는 PD-L1의 비전통적 역신호 전달(PDK4/GLS 대사 축 활성화 [40]), PCSK9 매개 Cystine 대사 재프로그래밍 [46], PRKX 매개 PD-L1 안정화 [31], 그리고 NKG2A-HLA-E 축을 통한 CD8+ T 세포 기능 저해 [13] 등 다양한 내성 기전을 제시하고 있습니다. 또한, 종양 미세환경(TME) 내 Neutrophil Senescence [63], B7-H3-MAPK 신호를 통한 MDSCs 및 CAFs의 면역억제 네트워크 강화 [19], 그리고 감각 신경을 통한 T-cell exhaustion 유도 [56] 등이 치료 실패의 원인으로 지적됩니다. 이러한 복잡한 다중 경로 내성 기전을 동시에 표적할 수 있는 효과적인 Combination Therapy 전략이 시급히 필요합니다.

2. '면역학적으로 차가운(Cold)' 종양 및 난치성 하위 그룹에 대한 유효한 치료 옵션 부족 PD-L1 음성 NSCLC [69], MSS(Microsatellite Stable) 대장암 [65], 그리고 KRAS-mutant 폐선암 [61] 등 기존 면역요법에 반응이 낮은 환자군에서 새로운 치료 표적의 필요성이 강조됩니다. 특히, KRAS 신호가 PRTN3를 통해 CXCL9를 분해하여 T 세포 침입을 방해하는 기전 [61]이나, 위암에서 Neutrophil-epithelial niches가 B-cell 성숙을 저해하는 문제 [47]는 기존 CPI 요법의 한계를 보여줍니다. 이에 따라 Invikafusp alfa와 같은 비특이적 이중 T 세포 작용제 [43]나, CAR-NK 세포의 기능 강화(PTPN22 억제 [64], IL-12 공간적 제한 [3]) 등 새로운 Cellular Therapy 및 Bispecific Antibody 개발이 이러한 'Cold' 종양을 'Hot'하게 전환하기 위한 핵심 과제로 대두되고 있습니다.

3. AI 기반 영상 진단 및 예후 예측 모델의 임상 적용 장벽과 방법론적 신뢰성 문제 Deep Learning 및 Radiomics를 활용한 영상 분석은 ILA [6], Glioblastoma 재발 예측 [33], 췌장암 TLS 예측 [29] 등에서 높은 잠재력을 보이지만, 실제 임상 현장으로의 통합에는 여전히 격차가 존재합니다. 특히, 전처리 과정에서의 데이터 누출(Data Leakage)로 인한 모델 성능 과대평가 문제 [59]와, 알고리즘 Drift 모니터링 및 Post-market surveillance의 부재 [22]는 AI 도구의 신뢰성을 위협합니다. 또한, Automation Bias 방지를 위한 'Human-in-the-loop' 접근법의 필요성 [22]과, 다양한 벤더 간 표준화되지 않은 영상 데이터로 인한 일반화 가능성(Generalizability) 부족은 AI 기반 정밀의학 도구 도입의 주요 장벽으로 지적됩니다.

4. 종양 미세환경(TME) 내 비암세포 요소(Stromal cells, Immune cells)에 대한 정밀 표적 치료의 미흡 종양 세포 자체뿐만 아니라 TME를 구성하는 대식세포, 섬유아세포(CAFs), NK 세포, 그리고 신경 세포 등이 치료 반응과 내성에 결정적인 역할을 함에도 불구하고, 이를 효과적으로 조절하는 전략은 아직 초기 단계입니다. 예를 들어, B7-H3 표적 요법에 대한 CAFs 매개 저항성 [19], HIF1A+CSF3R+ Neutrophils에 의한 Hypoxic niche 형성 [34], 그리고 Tertiary Lymphoid Structures(TLS)의 기능적 상태(단순 존재 여부 vs. 기능적 활성)가 치료 반응을 결정한다는 점 [47, 51]이 강조됩니다. 따라서, TLS 기능을 향상시키거나 [27, 38], 특정 면역 세포 군집(Neutrophils, MDSCs)을 재프로그래밍하는 표적 요법의 개발이 필요합니다.

5. 뇌전이 및 뇌막 전이 등 중추신경계(CNS) 침범 환자에서의 진단 및 치료 한계 NSCLC 뇌전이 환자에서 ADC의 효능은 입증되었으나 [8], 여전히 CNS disease control을 위한 최적의 전략은 논쟁의 여지가 있습니다. 특히, 폐암 환자의 뇌막 전이는 예후가 나쁘며 기존 MRI와 CSF 세포학의 민감도가 낮아 조기 진단이 어렵습니다 [49]. Liquid Biopsy(CSF ctDNA, CTC)를 통한 정밀 진단과 치료 모니터링의 필요성이 제기되고 있으나, 아직 표준화된 프로토콜이 부재합니다. 또한, 초기 NSCLC에서 SBRT와 Immunotherapy의 병용 요법이 OS 향상을 보이지 않고 오히려 독성을 증가시킨 사례 [35]는 CNS 침범 또는 국소 진행성 질환에서의 치료 조합 최적화에 대한 추가 연구가 필요함을 시사합니다.

우선순위 제언

  1. Immunotherapy Resistance 기전의 다중 표적 병용 요법 개발: PD-L1 역신호 전달, 대사 재프로그래밍(PCSK9, PDK4/GLS), 그리고 TME 내 면역억제 세포(Neutrophils, MDSCs)를 동시에 차단하는 Combination Therapy 전략이 가장 시급합니다. 특히, KRAS-mutant 및 MSS 대장암 등 'Cold' 종양에서 CPI 효능을 증진시킬 수 있는 새로운 표적(PRTN3, Propionate 경로 등)에 대한 임상 시험이 우선되어야 합니다.
  2. AI 영상 모델의 방법론적 검증 및 표준화: Radiomics 및 Deep Learning 모델의 임상 적용 전, 데이터 누출(Data Leakage) 방지와 외부 검증 코호트를 통한 Robustness 평가가 필수적입니다. 또한, Post-market surveillance 체계 구축과 'Human-in-the-loop' 프로토콜 도입을 통해 AI 도구의 안전성과 신뢰성을 확보해야 합니다.
  3. CNS 침범 환자용 정밀 진단 및 치료 가이드라인 수립: 뇌막 전이 등 CNS 질환에서 Liquid Biopsy의 임상 유용성을 입증하고, ADC 및 Bispecific Antibody가 CNS 장벽을 통과하여 효능을 발휘할 수 있는지 명확히 하는 연구가 필요합니다. 또한, SBRT와 Immunotherapy 병용 요법의 안전성 프로필을 재평가하여 고위험 초기 NSCLC 환자에서의 최적 치료 시기를 결정해야 합니다.

근거 논문

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