6  Mammella — Casi MTB (HR+/HER2− e TNBC)

library(readr); library(dplyr); library(gt); library(stringr); library(tidyr)

# ===== Helpers per robustezza =====
standardize_cols <- function(df) {
  mapping <- list(
    case                = c("case","Case","cohort","subset","Group","group"),
    gene                = c("gene","Gene","Hugo_Symbol","Symbol"),
    variant             = c("variant","Variant","cDNA","cdna_change","hgvsc","hgvsc_short","c_change","mutation","c_variant","c_hgvs"),
    protein_change      = c("protein_change","Protein","hgvsp","hgvsp_short","p_change","aa_change","p_hgvs","protein"),
    effect              = c("consequence","effect","variant_effect","Consequence"),
    classification      = c("classification","class","pathogenicity","clin_sig","clinical_significance","significance"),
    escat               = c("escat","ESCAT"),
    tier_amp_asco_cap   = c("tier_amp_asco_cap","tier","tier_amp","amp_asco_cap_tier","amp_asco_cap"),
    vaf                 = c("vaf","VAF","allele_fraction","variant_allele_frequency","AF"),
    depth               = c("depth","coverage","dp","read_depth","DP"),
    pd_l1_cps           = c("pd_l1_cps","PDL1_CPS","PDL1","pd-l1","pdl1"),
    msi_status          = c("msi_status","MSI","msi","mismatch_repair"),
    tmb                 = c("tmb","TMB","tumor_mutational_burden","mutational_burden"),
    her2_ihc            = c("her2_ihc","HER2_IHC","ihc_her2"),
    her2_ish            = c("her2_ish","HER2_ISH","fish_her2"),
    notes               = c("notes","note","comment","comments","interpretation","report_note")
  )
  for (tgt in names(mapping)) {
    hit <- mapping[[tgt]][mapping[[tgt]] %in% names(df)]
    if (length(hit) >= 1) df[[tgt]] <- df[[hit[1]]] else df[[tgt]] <- NA
  }
  df
}

# carica dati
som1  <- read_csv("data/breast_variants_somatic.csv", show_col_types = FALSE) 
som <- som1 %>%
  rename(
    variant         = variant_cDNA,
    protein_change  = variant_protein,
    tier_amp_asco_cap = tier_amp_ascocap,   # corregge il typo
    notes           = evidence_summary
  ) %>%
  mutate(
    VAF        = suppressWarnings(as.numeric(vaf)),
    Copertura  = suppressWarnings(as.numeric(depth))
  )

# (opzionale) se vuoi tenere anche le copy-number in tabella
# le rinominiamo in "CN" per chiarezza:
if ("copy_number" %in% names(som)) {
  som <- som %>% rename(CN = copy_number)
}

# ---- FUNZIONE TABELLA ROBUSTA ----
mk_table <- function(df, caption) {
  # seleziona solo le colonne che esistono
  disp <- df %>%
    select(any_of(c(
      "gene","variant","protein_change","effect","CN",
      "escat","tier_amp_asco_cap","VAF","Copertura","notes"
    ))) %>%
    rename(
      Gene      = gene,
      Variante  = variant,
      Proteina  = protein_change,
      Effetto   = effect,
      ESCAT     = escat,
      Tier      = tier_amp_asco_cap,
      Note      = notes
    )

  gt(disp) |>
    tab_caption(caption) |>
    fmt_number(columns = any_of(c("VAF")), decimals = 3) |>
    tab_spanner(label = "Qualità", columns = intersect(c("VAF","Copertura"), names(disp))) |>
    tab_source_note(md("**Nota**: VAF/Copertura mostrate solo se presenti."))
}

som<-som |> standardize_cols()

germ <- read_csv("data/breast_variants_germline.csv", show_col_types = FALSE) |> standardize_cols()

# coerenza tipi numerici dove esistono
som <- som |>
  mutate(vaf_num = suppressWarnings(as.numeric(vaf)),
         depth_num = suppressWarnings(as.numeric(depth)),
         tmb_num = suppressWarnings(as.numeric(tmb)),
         pd_l1_cps_num = suppressWarnings(as.numeric(pd_l1_cps)))

germ <- germ %>%
  # crea le colonne numeriche solo se quelle originali esistono
  mutate(across(any_of(c("vaf","depth")),
                ~ suppressWarnings(as.numeric(.x)),
                .names = "{.col}_num"))  #suppressWarnings(as.numeric(depth)) else NA_real_)

# utility per rendering tabella pulita (funziona anche se VAF/Depth non esistono)
mk_table <- function(df, caption) {
  # assicura colonne opzionali
  if (!("vaf_num" %in% names(df)))   df$vaf_num <- NA_real_
  if (!("depth_num" %in% names(df))) df$depth_num <- NA_real_
  disp <- df |>
    select(any_of(c("gene","variant","protein_change","effect","classification",
                    "escat","tier_amp_asco_cap","vaf_num","depth_num","notes"))) |>
    rename(Gene = gene, Variante = variant, Proteina = protein_change, Effetto = effect,
           Classe = classification, ESCAT = escat, Tier = tier_amp_asco_cap,
           VAF = vaf_num, Copertura = depth_num, Note = notes)
  gt(disp) |>
    tab_caption(caption) |>
    fmt_number(columns = c("VAF"), decimals = 3) |>
    tab_spanner(label = "Qualità", columns = c("VAF","Copertura")) |>
    tab_source_note(md("**Nota**: VAF e copertura sono riportate solo se presenti nei file sorgente."))
}

6.1 HR+/HER2− — Varianti di interesse

som %>% filter(case %in% c("HRpos_HER2neg","HR+_HER2-","HRpos")) %>% select(-any_of("case")) %>%
  mk_table("HR+/HER2− — Varianti somatiche")
HR+/HER2− — Varianti somatiche
Gene Variante Proteina Effetto Classe ESCAT Tier
Qualità
Note
VAF Copertura
PIK3CA c.3140A>G p.H1047R Activating NA I-A Tier IA 0.310 480 Enables PI3K inhibitor (alpelisib) with endocrine therapy.
ESR1 c.1610A>C p.Y537S Activating NA II Tier IIA 0.120 420 Resistance marker; SERD consideration.
Nota: VAF e copertura sono riportate solo se presenti nei file sorgente.

6.1.1 Suggerimenti clinici (HR+/HER2−, aggiornati al 2025)

  • ESR1 mutato → elacestrant (SERD orale) in progressione a ET/CDK4/6 se ESR1+ (test liquido o tessuto con CDx). [FDA 27-01-2023]
    Note: utilità maggiore dopo CDK4/6i e in malattia sensibile all’ET.
  • PIK3CA/AKT1/PTEN alterati → capivasertib + fulvestrant (post-ET) o alpelisib + ET per PIK3CA mut selezionate.
  • HER2-low (IHC 1+ o 2+/ISH−) → nelle linee successive, considerare trastuzumab deruxtecan (T‑DXd).
  • MSI‑H/TMB‑high (raro) → opzione pembrolizumab tissue‑agnostic.
  • NTRK fusion (rarissimo) → larotrectinib o entrectinib.

Razionale/Fonti principali: ESMO Living Guideline 2025 HR+/HER2−; FDA label elacestrant; FDA label capivasertib; DESTINY‑Breast04 per T‑DXd.

6.2 TNBC — Varianti di interesse

som %>% filter(case %in% c("TNBC","TripleNegative")) %>% select(-any_of("case")) %>%
  mk_table("TNBC — Varianti somatiche")
TNBC — Varianti somatiche
Gene Variante Proteina Effetto Classe ESCAT Tier
Qualità
Note
VAF Copertura
BRCA1 c.5266dupC p.Gln1756Profs*74 Pathogenic (LOF) NA I-A Tier IA 0.440 500 PARP inhibitor eligibility in germline carriers.
Nota: VAF e copertura sono riportate solo se presenti nei file sorgente.

6.2.1 Suggerimenti clinici (TNBC, aggiornati al 2025)

  • PD‑L1 CPS ≥10 (prima linea metastatica) → pembrolizumab + chemioterapia (KEYNOTE‑355).
  • Dopo ≥1 linea (HR−/HER2−): sacituzumab govitecan (Trop‑2 ADC) ha dimostrato beneficio OS vs TPC.
  • gBRCA1/2 → PARP inibitori (olaparib/talazoparib).
  • HER2‑low (sottogruppo di TNBC) → candidabile a T‑DXd in linee successive.
  • NTRK fusions (raro) → larotrectinib/entrectinib.
  • MSI‑H/TMB‑high → pembrolizumab tissue‑agnostic.

Razionale/Fonti principali: KEYNOTE‑355 per pembrolizumab; ASCENT/TROPiCS‑02 e aggiornamenti 2025 per sacituzumab govitecan; DESTINY‑Breast04 per T‑DXd.

6.3 Germinale

mk_table(germ, "Mammella — Varianti germinali")
Mammella — Varianti germinali
Gene Variante Proteina Effetto Classe ESCAT Tier
Qualità
Note
VAF Copertura
BRCA1 NA NA NA Pathogenic NA NA NA NA Recommend germline testing and genetic counseling.
— NA NA NA No pathogenic variants suspected NA NA NA NA Germline per storia personale/familiare.
Nota: VAF e copertura sono riportate solo se presenti nei file sorgente.
Nota

Indicazioni pratiche
- PIK3CA p.H1047R → considerare alpelisib + ET (alternativa a capivasertib+fulvestrant in HR+/HER2− con PIK3CA).
- ESR1 patogena → elacestrant dopo ET/CDK4/6i.
- BRCA1/2 patogena (specialmente TNBC) → PARP; invio a genetica medica e cascade testing.
- PD‑L1 CPS ≥10 in TNBC → pembrolizumab + chemio in prima linea.
- HER2‑low → T‑DXd in setting avanzato dopo almeno una chemioterapia.

6.4 Riferimenti (principali, per pronta consultazione)

  • ESMO Living Guideline (Aprile 2025) — HR+/HER2− MBC: https://www.esmo.org/guidelines/living-guidelines/esmo-living-guideline-metastatic-breast-cancer/hr-positive-her2-negative-metastatic-breast-cancer/hr-her2-mbc-progressive-disease-candidate-for-endocrine-therapy-targeted-therapy
  • FDA — Elacestrant (ESR1‑mut, ER+/HER2−): https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-elacestrant-er-positive-her2-negative-esr1-mutated-advanced-or-metastatic-breast-cancer
  • FDA — Capivasertib + fulvestrant (PIK3CA/AKT1/PTEN‑alter): https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-capivasertib-fulvestrant-breast-cancer
  • DESTINY‑Breast04 — T‑DXd in HER2‑low: https://www.nejm.org/doi/full/10.1056/NEJMoa2203690
  • KEYNOTE‑355 — Pembrolizumab + chemio in TNBC PD‑L1 CPS≥10: https://pubmed.ncbi.nlm.nih.gov/35857659/
  • TROPiCS‑02 / ASCENT — Sacituzumab govitecan: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736%2823%2901245-X/fulltext ; https://ascopubs.org/doi/10.1200/JCO.2023.41.16_suppl.1003 ; https://pmc.ncbi.nlm.nih.gov/articles/PMC12257498/