Additional file 3 of Homologous recombination deficiency (HRD) can predict the therapeutic outcomes of immuno-neoadjuvant therapy in NSCLC patients
Creators: name: Zhou, Zhen
name: Ding, Zhengping
name: Yuan, Jie
name: Shen, Shengping
name: Jian, Hong
name: Tan, Qiang
name: Yang, Yunhai
name: Chen, Zhiwei
name: Luo, Qingquan
name: Cheng, Xinghua
name: Yu, Yongfeng
name: Niu, Xiaomin
name: Qian, Liqiang
name: Chen, Xiaoke
name: Gu, Linping
name: Liu, Ruijun
name: Ma, Shenglin
name: Huang, Jia
name: Chen, Tianxiang
name: Li, Ziming
name: Ji, Wenxiang
name: Song, Liwei
name: Shen, Lan
name: Jiang, Long
name: Yu, Zicheng
name: Zhang, Chao
name: Tai, Zaixian
name: Wang, Changxi
name: Chen, Rongrong
name: Carbone, David P.
name: Xia, Xuefeng
name: Lu, Shun
Publisher: name: figshare
Year: 2022
Subjects: subject: Biochemistry
subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Pharmacology
subject: Immunology
subject: FOS: Clinical medicine
subject: Cancer

description: Additional file 3: Supplementary Figures. Figure S1. Mutation statistics of all curated samples. (A) Regimen combinations of the 14 NSCLC patients. (B) Chemotherapeutic drugs and immunotherapy agents used by each patient. (C) Neoadjuvant immunotherapy duration time distribution between MPR and Non-MPR groups. (D) Correlation between PD-L1 expression and percentage of viable tumor cells in patients. (E) Mutation number retained after filtrations. (F) Mutation number distribution on MPR/Non-MPR and FFPE/Frozen specimen. (G) Six substitution type spectrum plot in two patient groups. P-values were shown above each substitution category. (H) Exposures of three types of mutation signature to known database. SBS: single base substitution; ID: InDel; DBS: double base substitution. Either sum or log2 fold change of the absolute exposures was addedly depicted. (I) Percentage of altered tumor suppressor genes enriched in multiple DNA repair-related pathways in MPR and Non-MPR group. Figure S2. SCNA statistical and signature analyses results in groups with distinct therapeutic response. (A) Chromosome level CNV burden between MPR/Non-MPR groups. (B) Arm level CNV burden in two groups. (C) Chromosome level CNV burden between squamous cell carcinoma (SCC) MPR/Non-MPR patients. (D) Arm level CNV burden in two SCC patient groups. (E) Correlation between chromosome level CNV burden and percentage of viable tumor cells. (F) Correlation between arm level CNV burden and percentage of viable tumor cells. (G) Percentage of genome amplified in two groups. (H) Percentage of genome deleted in two groups. (I) Percentage of genome amplified in SCC patients. (J) Percentage of genome deleted in SCC patients. (K) SCNA heatmap only plotting copy number alternations exceeding the threshold +/-0.2. Two aneuploid samples were marked by pink circles. (L) Contributions of three SCNA signatures in all samples. Adenocarcinoma samples were marked with pink triangles. (M) Proportion of SCNA Signature 1 in patients. (N) Proportion of SCNA Signature 7 in patients. (O) Proportion of SCNA Signature 5 in patients. (P) Proportion of SCNA Signature 1 in SCC patients. (Q) Proportion of SCNA Signature 7 in SCC patients. (R) Proportion of SCNA Signature 5 in SCC patients. Figure S3. HRDscore in non-aneuploid SCC samples. (A) HRDscore calculated on non-aneuploid SCC samples. (B) Correlation between HRDscore and percentage of viable tumor cells in all non-aneuploid SCC samples. Figure S4. Multidimensional ITH comparison results between MPR/Non-MPR groups. (A) TMB value distribution in SCC samples. (B) Correlation between TMB and percentage of viable tumor cells in SCC samples. (C) Clonal TMB value in two groups, only selecting SCC patients. (D) Correlation between clonal TMB and percentage of viable tumor cells on SCC samples. (E) Germline HR pathway gene mutation number in MPR/Non-MPR groups. (F) Subclonal SCNA fragment number in two groups, only selecting SCC samples. (G) Correlation between subclonal SCNA fragment number and percentage of viable tumor cells, only selecting SCC samples. Figure S5. Investigation on the neoantigen load difference between patients with distinct outcomes. (A) TNB in two groups. (B) Correlation between TNB values and percentage of viable tumor cells. (C) TNB in two groups, only selecting SCC samples. (D) Correlation between TNB values and percentage of viable tumor cells, only selecting SCC samples. (E) Clonal TNB value distribution in two groups, only selecting SCC samples. (F) Correlation between clonal TNB and percentage of viable tumor cells, only selecting SCC samples. (G) HLA LOH frequency in MPR/Non-MPR groups. (H) TNB value on kept HLA alleles. (I) Correlation between TNB on kept HLA alleles and percentage of viable tumor cells. (J) TNB value on kept HLA alleles, only selecting SCC samples. (K) Correlation between TNB on kept HLA alleles and percentage of viable tumor cells, only selecting SCC samples. (L) Clonal TNB value on kept HLA alleles. (M) Correlation between clonal TNB on kept HLA alleles and percentage of viable tumor cells. (N) Clonal TNB value on kept HLA alleles, only selecting SCC samples. (O) Correlation between clonal TNB on kept HLA alleles and percentage of viable tumor cells, only selecting SCC samples. Figure S6. Comprehensive analysis on clonal neoantigen and SCNA bring insights to the function of SCNA on neoantigen generation. (A) Percentage of kept clonal neoantigens on SCNA regions in two groups. (B) Correlation between CNV-related neoantigen proportion and percentage of viable tumor cells. (C) Percentage of kept clonal neoantigens on SCNA regions in two groups, only selecting SCC samples. (D) Correlation between CNV-related neoantigen proportion and percentage of viable tumor cells, only selecting SCC samples. (E) Number of clonal neoantigen on kept HLA alleles and their distribution on 3 types of SCNAs. Adenocarcinoma samples were marked with pink triangles. The sample with ATR mutation was marked with arrow. (F) Correlation between clonal neoantigen number on amplified segments and percentage of viable tumor cells. (G) Correlation between clonal neoantigen number on amplified segments and percentage of viable tumor cells, only selecting SCC samples. (H) Correlation values on all 11 non-aneuploid and high purity samples, calculated by Spearman Correlation Coefficient (SCC). (I) Similar with (H), the correlation values were on MPR samples. (J) Similar with (H), the correlation values were on Non-MPR samples. Figure S7. Investigation on HR pathway gene alternation frequency in patients with distinct response from public cohorts. (A) HR pathway gene mutation frequency in J Immunother Cancer. 2020 neoadjuvant dataset. Left: in pCR patients. Middle: in other patients. Right: percentage of distinct mutant reads for each HR gene mutation detected. Mutations from pCR patients were marked with red color. (B) HR pathway gene mutation frequency in N Engl J Med. 2018 neoadjuvant dataset. Left: in MPR patients. Middle: in Non-MPR patients. Right: percentage of distinct mutant reads for each HR gene mutation detected. Mutations from MPR patients were marked with red color. (C) HR pathway gene mutation frequency in DCB patients from Nat Genet. 2018 dataset. Left: all and clonal HR gene mutations. Right: all and clonal HR core pathway mutations. (D) Similar with (C), but using non-DCB patients. (E) Similar with (C), but using J Clin Oncol. 2018 dataset. (F) Similar with (C), but using NDB patients. (G) HR pathway gene mutation frequency in patients achieved DCB in chemotherapy from Cancer Discov. 2017 dataset. (H) Similar with (G), but in NDB patients. (I) HR pathway gene SCNA frequency in patients achieved DCB in chemotherapy from Cancer Discov. 2017 dataset. Left: HR gene SCNAs. Right: HR core pathway gene SCNAs. (J) Similar with (I), but in NDB patients. (K) HR pathway gene mutation frequency in patients achieved CR and PR in chemotherapy from Nat Med. 2018 dataset. Left: all and clonal HR gene mutations. Right: all and clonal HR core pathway mutations. (L) Similar with (K), but in other patients. (M) Similar with (K), but using patients received immunotherapy. (N) Similar with (M), but in other patients. Figure S8. Comparisons on TMB values stratified by therapeutic response and HR pathway gene status from public cohorts. (A) TMB and clonal TMB value distribution between DCB and other patients in non-squamous population from Nat Genet. 2018 dataset. Left: TMB. Right: clonal TMB. (B) Similar with (A) but using HR pathway gene clonal mutations as the stratification strategy. (C) TMB value distribution between DCB and NDB patients in J Clin Oncol. 2018. (D) Similar with (C) but focusing on different histological subtypes. Left: in adenocarcinoma subgroup. Right: in squamous subgroup. (E) Similar with (D) but using HR pathway gene mutation as the classification strategy. (F) Clonal TMB value distribution between patients stratified by HRD event in J Clin Oncol. 2018 dataset. Left: between patients with and without HR pathway gene mutation. Right: between patients with and without HR pathway gene clonal mutation. (G) TMB value distribution between CR+PR and other immunotherapy patients in Nat Med. 2018 dataset. (H) Similar with (G) but focusing on different histological subtypes. Left: in non-squamous subgroup. Right: in squamous subgroup. (I) TMB value distribution between CR+PR patients with HRD and other CR+PR patients in Nat Med. 2018. (J) Similar with (F), but in Nat Med. 2018 dataset. Figure S9. Survival analyses and TMB comparisons on HRD event-stratified patients in multiple public cohorts. (A) PFS of patients stratified by HR gene mutational condition in Nat Genet. 2018 dataset. Left: between patients with and without HR gene mutation. Right: between patients with and without HR gene clonal mutation. (B) Similar with (A), but on OS data. (C) Similar with (A) but in all patients from J Clin Oncol. 2018 cohort. (D) Similar with (C) but in adenocarcinoma patients. (E) Similar with (C), but in squamous patients. (F) PFS and TMB of all DCB patients with and without HR gene mutations in Nat Genet. 2018 cohort. (G) Similar with (F) but on OS data. (H) Similar with (F) but using HR pathway gene clonal mutations as the classification strategy. (I) Similar with (H) but on OS data. (J) Similar with (F) but in patients from J Clin Oncol. 2018 cohort. Figure S10. Survival analyses and TMB comparisons conducted on Nat Med. 2018 blood cohort. (A) Survival and TMB of all chemo-treated non-squamous patients with and without HR gene mutations. Left: OS. Middle: PFS. Right: TMB distribution. (B) Similar with (A) but on squamous patients. (C) Similar with (A) but on patients received immunotherapy. (D) Similar with (C) but on squamous patients. (E) Survival and TMB of all HR pathway-mutant patients treated with immunotherapy and chemotherapy. Left: OS. Middle: PFS. Right: TMB distribution. (F) Similar with (E) but among patients with HR gene clonal mutations. Figure S11. Investigation on HR pathway gene alternation frequency and HRD events in multiracial untreated patients. (A) HR pathway gene mutation frequency in Sci Rep. 2015 dataset. Left: all and clonal HR gene mutations. Right: all and clonal HR core pathway mutations. (B) Similar with (A), but in J Thorac Oncol. 2020 cohort. (C) Similar with (A), but in TCGA-LUAD dataset. (D) Similar with (A), but in TCGA-LUSC dataset. (E) HR pathway gene alternation frequency in TCGA-LUAD dataset. (F) Similar with (E), but in TCGA-LUSC dataset. (G) Correlation between CNV burden and HRDscore in TCGA-LUAD dataset. (H) Similar with (G), but in TCGA-LUSC dataset.